Uses of Interface
dev.nm.algebra.linear.vector.doubles.Vector
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles
Methods in dev.nm.algebra.linear.matrix.doubles that return Vector Modifier and Type Method Description Vector
ImmutableMatrix. getColumn(int j)
Vector
Matrix. getColumn(int j)
Get the specified column in the matrix as a vector.Vector
ImmutableMatrix. getRow(int i)
Vector
Matrix. getRow(int i)
Get the specified row in the matrix as a vector.Vector
ImmutableMatrix. multiply(Vector v)
Vector
Matrix. multiply(Vector v)
Right multiply this matrix, A, by a vector.Methods in dev.nm.algebra.linear.matrix.doubles with parameters of type Vector Modifier and Type Method Description static boolean
MatrixPropertyUtils. areAllSparse(Vector... vectors)
Checks if all vectors are SparseVector.static boolean
MatrixPropertyUtils. areEqual(Vector v1, Vector v2, double epsilon)
Checks if two vectors are equal, i.e., v1 - v2 is a zero vector, up to a precision.static boolean
MatrixPropertyUtils. areOrthogonal(Vector[] v, double epsilon)
Checks if a set of vectors are orthogonal, i.e., for any v1, v2 in v, v1 ∙ v2 == 0.static boolean
MatrixPropertyUtils. areOrthogonal(Vector v1, Vector v2, double epsilon)
Checks if two vectors are orthogonal, i.e., v1 ∙ v2 == 0.static boolean
MatrixPropertyUtils. areOrthogonormal(Vector[] v, double epsilon)
Checks if a set of vectors are orthogonormal.static boolean
MatrixPropertyUtils. areOrthogonormal(Vector v1, Vector v2, double epsilon)
Checks if two vectors are orthogonormal.static boolean
MatrixPropertyUtils. isNaN(Vector v)
Checks if a vector contains anyNaN
entry.static boolean
MatrixPropertyUtils. isZero(Vector v, double epsilon)
Checks if a vector is a zero vector, i.e., all its entries are 0, up to a precision.Vector
ImmutableMatrix. multiply(Vector v)
Vector
Matrix. multiply(Vector v)
Right multiply this matrix, A, by a vector.static void
MatrixPropertyUtils. throwIfInvalidIndex(Vector v, int index)
Checks if an index is a valid index.static void
MatrixPropertyUtils. throwIfNotEqualSize(Vector v1, Vector v2)
Checks if the input vectors have the same size. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen that return Vector Modifier and Type Method Description Vector
EigenProperty. eigenVector()
Get an eigenvector.Vector
InverseIteration. getEigenVector()
Get an eigenvector.Vector
InverseIteration. getEigenVector(Vector v0, int maxIterations)
Get an eigenvector from an initial guess.Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen that return types with arguments of type Vector Modifier and Type Method Description List<Vector>
EigenProperty. eigenbasis()
Get the eigenvectors.Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen with parameters of type Vector Modifier and Type Method Description Vector
InverseIteration. getEigenVector(Vector v0, int maxIterations)
Get an eigenvector from an initial guess.boolean
InverseIteration.StoppingCriterion. toStop(Vector v)
Check whether we stop with the current eigenvector. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds
Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.dqds with parameters of type Vector Constructor Description DQDS(int n, Vector q, Vector e)
Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with theq
ande
values to high relative accuracy. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 that return Vector Modifier and Type Method Description Vector
LDDecomposition. D()
The diagonal entries of the diagonal matrix D.Vector
LDFactorizationFromRoot. D()
Vector
LDDecomposition. L()
The sub-diagonal entries of the unit lower triangular matrix L.Vector
LDFactorizationFromRoot. L()
Vector
LDDecomposition. LD()
Vector
LDDecomposition. LLD()
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 that return types with arguments of type Vector Modifier and Type Method Description List<Vector>
MR3. getEigenvectors()
Gets all the eigenvectors which corresponds to the list of eigenvalues.List<Vector>
SymmetricEigenByMR3. getEigenvectors()
List<Vector>
SymmetricEigenFor2x2Matrix. getEigenvectors()
Gets the eigenvectors.Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 with parameters of type Vector Modifier and Type Method Description int[]
BlockSplitPointSearch. compute(Vector D, Vector E, Vector E2)
Searches splitting points in the symmetric tridiagonal matrix.static RealInterval[]
EigenBoundUtils. computeGershgorinIntervals(Vector D, Vector E)
Computes the Gershgorin bounds for all eigenvalues in a symmetric tridiagonal matrix T.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 with parameters of type Vector Constructor Description EigenCount(Vector D, Vector E2)
Creates an instance for a symmetric tridiagonal matrix T.EigenCountInRange(Vector D, Vector E2)
Creates an instance for counting the number of eigenvalues of the symmetric tridiagonal matrix T that are in a given interval.LDDecomposition(Vector D, Vector L, double sigma)
LDFactorizationFromRoot(Vector D, Vector E, double sigma)
Creates a decomposition for a symmetric tridiagonal matrix T.MR3(Vector D, Vector E)
Creates an instance for computing eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T.MR3(Vector D, Vector E, boolean wantEigenvector)
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T.MR3(Vector D, Vector E, boolean wantEigenvector, double minRelativeGap)
Creates an instance for computing eigenvalues (and eigenvectors) of a given symmetric tridiagonal matrix T. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec that return Vector Modifier and Type Method Description Vector
Getvec. getEigenvector()
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr that return types with arguments of type Vector Modifier and Type Method Description List<Vector>
SymmetricQRAlgorithm. getEigenvectors()
Gets the eigenvectors of A, which are the columns of Q.List<Vector>
QRAlgorithm. getEigenVectors()
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.linearsystem
Methods in dev.nm.algebra.linear.matrix.doubles.linearsystem that return Vector Modifier and Type Method Description Vector
LSProblem. getInitialGuess()
Gets the initial guess of the solution for the problem.Vector
LinearSystemSolver.Solution. getParticularSolution(Vector b)
Get a particular solution for the linear system.Vector
BackwardSubstitution. solve(UpperTriangularMatrix U, Vector b)
Solve Ux = b.Vector
BackwardSubstitution. solve(UpperTriangularMatrix U, Vector b, double epsilon)
Vector
ForwardSubstitution. solve(LowerTriangularMatrix L, Vector b)
Solve Lx = b.Vector
ForwardSubstitution. solve(LowerTriangularMatrix L, Vector b, double epsilon)
Vector
LUSolver. solve(LSProblem problem)
Solve Ax = b.Vector
OLSSolverByQR. solve(LSProblem problem)
In the ordinary least square sense, solve Ax = yVector
OLSSolverBySVD. solve(LSProblem problem)
In the ordinary least square sense, solve Ax = yVector
ThomasAlgorithm. solve(TridiagonalMatrix A, Vector d)
Solves a tridiagonal matrix equation.Methods in dev.nm.algebra.linear.matrix.doubles.linearsystem that return types with arguments of type Vector Modifier and Type Method Description List<Vector>
Kernel. basis()
Get the kernel basis.Map<Integer,Vector>
Kernel. basisAndFreeVars()
Get the kernel basis and the associated free variables for each basis/column.List<Vector>
LinearSystemSolver.Solution. getHomogeneousSoln()
Get the basis of the homogeneous solution for the linear system, Ax = b.Methods in dev.nm.algebra.linear.matrix.doubles.linearsystem with parameters of type Vector Modifier and Type Method Description Vector
LinearSystemSolver.Solution. getParticularSolution(Vector b)
Get a particular solution for the linear system.Vector
BackwardSubstitution. solve(UpperTriangularMatrix U, Vector b)
Solve Ux = b.Vector
BackwardSubstitution. solve(UpperTriangularMatrix U, Vector b, double epsilon)
Vector
ForwardSubstitution. solve(LowerTriangularMatrix L, Vector b)
Solve Lx = b.Vector
ForwardSubstitution. solve(LowerTriangularMatrix L, Vector b, double epsilon)
Vector
ThomasAlgorithm. solve(TridiagonalMatrix A, Vector d)
Solves a tridiagonal matrix equation.LSProblem
LSProblem. withInitialGuess(Vector initialGuess)
Overrides the initial guess of the solution.Constructors in dev.nm.algebra.linear.matrix.doubles.linearsystem with parameters of type Vector Constructor Description LSProblem(Matrix A, Vector b)
Constructs a system of linear equations Ax = b. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype that return Vector Modifier and Type Method Description Vector
DefaultMatrixStorage. getColumn(int j)
Get a column.Vector
GivensMatrix. getColumn(int j)
Vector
PermutationMatrix. getColumn(int j)
Vector
DefaultMatrixStorage. getRow(int i)
Get a row.Vector
GivensMatrix. getRow(int i)
Vector
PermutationMatrix. getRow(int i)
Vector
GivensMatrix. multiply(Vector v)
Vector
PermutationMatrix. multiply(Vector v)
Left multiplication by P.Vector
GivensMatrix. multiplyInPlace(Vector v)
Right multiplies this matrix, A, by a vector.Vector
GivensMatrix. rotate(Vector x)
Deprecated.Not supported yet.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype with parameters of type Vector Modifier and Type Method Description Vector
GivensMatrix. multiply(Vector v)
Vector
PermutationMatrix. multiply(Vector v)
Left multiplication by P.Vector
GivensMatrix. multiplyInPlace(Vector v)
Right multiplies this matrix, A, by a vector.Vector
GivensMatrix. rotate(Vector x)
Deprecated.Not supported yet.void
DefaultMatrixStorage. setColumn(int j, Vector v)
Set the values for a column in the matrix, i.e., [*, j].void
DefaultMatrixStorage. setRow(int i, Vector v)
Set the values for a row in the matrix, i.e., [i, *]. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense that return Vector Modifier and Type Method Description Vector
DenseMatrix. getColumn(int j)
Vector
DenseMatrix. getColumn(int j, int beginRow, int endRow)
Gets a sub-column of the j-th column, frombeginRow
row toendRow
row, inclusively.Vector
DenseMatrix. getRow(int i)
Vector
DenseMatrix. getRow(int i, int beginCol, int endCol)
Gets a sub-row of the i-th row, frombeginCol
column toendCol
column, inclusively.Vector
DenseMatrix. multiply(Vector v)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense with parameters of type Vector Modifier and Type Method Description Vector
DenseMatrix. multiply(Vector v)
void
DenseMatrix. setColumn(int j, Vector v)
Changes the matrix column values to a vector value.void
DenseMatrix. setRow(int i, Vector v)
Changes the matrix row values to a vector value.Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense with parameters of type Vector Constructor Description DenseMatrix(Vector v)
Constructs a column matrix from a vector. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal that return Vector Modifier and Type Method Description Vector
DiagonalMatrix. multiply(Vector v)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal with parameters of type Vector Modifier and Type Method Description Vector
DiagonalMatrix. multiply(Vector v)
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle that return Vector Modifier and Type Method Description Vector
LowerTriangularMatrix. getColumn(int j)
Vector
SymmetricMatrix. getColumn(int j)
Vector
UpperTriangularMatrix. getColumn(int j)
Vector
LowerTriangularMatrix. getRow(int i)
Vector
SymmetricMatrix. getRow(int i)
Vector
UpperTriangularMatrix. getRow(int i)
Vector
LowerTriangularMatrix. multiply(Vector v)
Vector
SymmetricMatrix. multiply(Vector v)
Vector
UpperTriangularMatrix. multiply(Vector v)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle with parameters of type Vector Modifier and Type Method Description Vector
LowerTriangularMatrix. multiply(Vector v)
Vector
SymmetricMatrix. multiply(Vector v)
Vector
UpperTriangularMatrix. multiply(Vector v)
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation that return Vector Modifier and Type Method Description Vector
AutoParallelMatrixMathOperation. multiply(MatrixAccess A, Vector v)
Vector
MatrixMathOperation. multiply(MatrixAccess A, Vector v)
A * vVector
SimpleMatrixMathOperation. multiply(MatrixAccess A, Vector v)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation with parameters of type Vector Modifier and Type Method Description Vector
AutoParallelMatrixMathOperation. multiply(MatrixAccess A, Vector v)
Vector
MatrixMathOperation. multiply(MatrixAccess A, Vector v)
A * vVector
SimpleMatrixMathOperation. multiply(MatrixAccess A, Vector v)
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse that implement Vector Modifier and Type Class Description class
SparseVector
A sparse vector stores only non-zero values.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse that return Vector Modifier and Type Method Description Vector
SparseVector. add(double c)
Vector
SparseVector. add(Vector that)
Vector
SparseVector. divide(Vector that)
Vector
SparseVector. leftMultiply(Matrix A)
Left multiplies a matrix.Vector
SparseVector. minus(double c)
Vector
SparseVector. minus(Vector that)
Vector
CSRSparseMatrix. multiply(Vector v)
Vector
DOKSparseMatrix. multiply(Vector v)
Vector
LILSparseMatrix. multiply(Vector v)
Vector
SparseVector. pow(double c)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse with parameters of type Vector Modifier and Type Method Description Vector
SparseVector. add(Vector that)
double
SparseVector. angle(Vector that)
Vector
SparseVector. divide(Vector that)
double
SparseVector. innerProduct(Vector that)
Vector
SparseVector. minus(Vector that)
Vector
CSRSparseMatrix. multiply(Vector v)
Vector
DOKSparseMatrix. multiply(Vector v)
Vector
LILSparseMatrix. multiply(Vector v)
SparseVector
SparseVector. multiply(Vector that)
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse with parameters of type Vector Constructor Description SparseVector(Vector v)
Constructs a sparse vector from a vector. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative that return Vector Modifier and Type Method Description Vector
IterativeLinearSystemSolver.Solution. search(Vector... initials)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative that return types with arguments of type Vector Modifier and Type Method Description IterationMonitor<Vector>
IterativeLinearSystemSolver.Solution. step()
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative with parameters of type Vector Modifier and Type Method Description Vector
IterativeLinearSystemSolver.Solution. search(Vector... initials)
Method parameters in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative with type arguments of type Vector Modifier and Type Method Description IterativeLinearSystemSolver.Solution
IterativeLinearSystemSolver. solve(LSProblem problem, IterationMonitor<Vector> monitor)
Solves iteratively Ax = b until the solution converges, i.e., the norm of residual (b - Ax) is less than or equal to the threshold. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
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Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner that return Vector Modifier and Type Method Description Vector
IdentityPreconditioner. solve(Vector x)
Return the input vector x.Vector
JacobiPreconditioner. solve(Vector x)
Return P-1x, where P is the diagonal matrix of A.Vector
Preconditioner. solve(Vector x)
Solve Mv = x, where M is the preconditioner matrix.Vector
SSORPreconditioner. solve(Vector x)
Solve Mz = x using this SSOR preconditioner.Vector
IdentityPreconditioner. transposeSolve(Vector x)
Return the input vector x.Vector
JacobiPreconditioner. transposeSolve(Vector x)
Pt = P-1 for Jacobi preconditioner.Vector
Preconditioner. transposeSolve(Vector x)
Solve Mtv = x, where M is the preconditioner matrix.Vector
SSORPreconditioner. transposeSolve(Vector x)
Mtx = M-1x as M is symmetric.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner with parameters of type Vector Modifier and Type Method Description Vector
IdentityPreconditioner. solve(Vector x)
Return the input vector x.Vector
JacobiPreconditioner. solve(Vector x)
Return P-1x, where P is the diagonal matrix of A.Vector
Preconditioner. solve(Vector x)
Solve Mv = x, where M is the preconditioner matrix.Vector
SSORPreconditioner. solve(Vector x)
Solve Mz = x using this SSOR preconditioner.Vector
IdentityPreconditioner. transposeSolve(Vector x)
Return the input vector x.Vector
JacobiPreconditioner. transposeSolve(Vector x)
Pt = P-1 for Jacobi preconditioner.Vector
Preconditioner. transposeSolve(Vector x)
Solve Mtv = x, where M is the preconditioner matrix.Vector
SSORPreconditioner. transposeSolve(Vector x)
Mtx = M-1x as M is symmetric. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary that return Vector Modifier and Type Method Description Vector
SORSweep. backward(Vector x)
Perform a backward sweep.Vector
SORSweep. forward(Vector x)
Perform a forward sweep.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary with parameters of type Vector Modifier and Type Method Description Vector
SORSweep. backward(Vector x)
Perform a backward sweep.Vector
SORSweep. forward(Vector x)
Perform a forward sweep.Method parameters in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary with type arguments of type Vector Modifier and Type Method Description IterativeLinearSystemSolver.Solution
GaussSeidelSolver. solve(LSProblem problem, IterationMonitor<Vector> monitor)
IterativeLinearSystemSolver.Solution
JacobiSolver. solve(LSProblem problem, IterationMonitor<Vector> monitor)
IterativeLinearSystemSolver.Solution
SuccessiveOverrelaxationSolver. solve(LSProblem problem, IterationMonitor<Vector> monitor)
IterativeLinearSystemSolver.Solution
SymmetricSuccessiveOverrelaxationSolver. solve(LSProblem problem, IterationMonitor<Vector> monitor)
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary with parameters of type Vector Constructor Description SORSweep(Matrix A, Vector b, double omega)
Construct an instance to perform forward or backward sweep for a linear system Ax = b. -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.operation
Classes in dev.nm.algebra.linear.matrix.doubles.operation that implement Vector Modifier and Type Class Description class
SVEC
SVEC
converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.Methods in dev.nm.algebra.linear.matrix.doubles.operation that return Vector Modifier and Type Method Description static Vector
MatrixUtils. colMeanVector(MatrixTable A)
Get the column mean vector of a given matrix.static Vector
MatrixUtils. colSumVector(MatrixTable A)
Get the column sum vector of a given matrix.Vector
ColumnBindMatrix. getColumn(int j)
Vector
DiagonalSum. getColumn(int j)
Vector
SubMatrixRef. getColumn(int j)
Vector
ColumnBindMatrix. getRow(int i)
Vector
DiagonalSum. getRow(int i)
Vector
SubMatrixRef. getRow(int i)
Vector
ColumnBindMatrix. multiply(Vector v)
Vector
DiagonalSum. multiply(Vector v)
Vector
FastKroneckerProduct. multiply(Vector v)
Vector
SubMatrixRef. multiply(Vector v)
static Vector
MatrixUtils. rowMeanVector(MatrixTable A)
Get the row mean vector of a given matrix.static Vector
MatrixUtils. rowSumVector(MatrixTable A)
Get the row sum vector of a given matrix.static Vector[]
MatrixUtils. toColumns(Matrix A)
Get an array of all column vectors from a matrix.static Vector[]
MatrixUtils. toRows(Matrix A)
Get an array of all row vectors from a matrix.Methods in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type Vector Modifier and Type Method Description static Matrix
MatrixFactory. cbind(Vector... vectors)
Combines an array of vectors by columns.Vector
ColumnBindMatrix. multiply(Vector v)
Vector
DiagonalSum. multiply(Vector v)
Vector
FastKroneckerProduct. multiply(Vector v)
Vector
SubMatrixRef. multiply(Vector v)
static Matrix
MatrixFactory. rbind(Vector... vectors)
Combines an array of vectors by rows.Method parameters in dev.nm.algebra.linear.matrix.doubles.operation with type arguments of type Vector Modifier and Type Method Description static Matrix
MatrixFactory. cbind(List<Vector> vectors)
Combines a list of vectors by columns.static Matrix
MatrixFactory. rbind(List<Vector> vectors)
Combines a list of array of vectors by rows.Constructors in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type Vector Constructor Description ColumnBindMatrix(Vector... vectors)
DiagonalSum(Matrix A, Vector d)
MAT(Vector v)
Constructs the MAT of a vector.OuterProduct(Vector a, Vector b)
VariancebtX(Vector b, Matrix X)
Computes \(b'Xb\). -
Uses of Vector in dev.nm.algebra.linear.matrix.doubles.operation.householder
Fields in dev.nm.algebra.linear.matrix.doubles.operation.householder declared as Vector Modifier and Type Field Description Vector
HouseholderContext. generator
The vector which is used to generate the Householder vector.Vector
HouseholderContext. v
The defining vector which is perpendicular to the Householder hyperplane.Methods in dev.nm.algebra.linear.matrix.doubles.operation.householder that return Vector Modifier and Type Method Description Vector
Householder4SubVector. definingVector()
Vector
HouseholderReflection. definingVector()
Get the Householder defining vector which is orthogonal to the Householder hyperplane.Vector
Householder4SubVector. reflect(Vector x)
Vector
Householder4ZeroGenerator. reflect(Vector x)
Vector
HouseholderReflection. reflect(Vector x)
Apply the Householder matrix, H, to a column vector, x.Methods in dev.nm.algebra.linear.matrix.doubles.operation.householder with parameters of type Vector Modifier and Type Method Description static HouseholderContext
HouseholderContext. getContext(Vector x)
Generates the context information from a generating vector x.Vector
Householder4SubVector. reflect(Vector x)
Vector
Householder4ZeroGenerator. reflect(Vector x)
Vector
HouseholderReflection. reflect(Vector x)
Apply the Householder matrix, H, to a column vector, x.void
HouseholderReflection. reflectVectors(Vector[] vectors, int startIndex, int endIndex)
Apply the Householder matrix, H, to an array of vectors.Constructors in dev.nm.algebra.linear.matrix.doubles.operation.householder with parameters of type Vector Constructor Description Householder4SubVector(int size, int startIndex, Vector v)
Householder4SubVector(int size, Vector v)
Householder4SubVector(int size, Vector v, double beta, double lambda)
HouseholderContext(Vector v, double beta, Vector generator, double lambda)
Constructs a Householder context information.HouseholderReflection(Vector v)
Construct a Householder matrix from the vector that defines the hyperplane orthogonal to the vector.HouseholderReflection(Vector v, double beta, double lambda)
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Uses of Vector in dev.nm.algebra.linear.vector.doubles
Classes in dev.nm.algebra.linear.vector.doubles that implement Vector Modifier and Type Class Description class
CombinedVectorByRef
For efficiency, this wrapper concatenates two or more vectors by references (without data copying).class
ImmutableVector
This is a read-only view of aVector
instance.class
SubVectorRef
Represents a sub-vector backed by the referenced vector, without data copying.Methods in dev.nm.algebra.linear.vector.doubles that return Vector Modifier and Type Method Description Vector
ImmutableVector. add(double scalar)
Vector
ImmutableVector. add(Vector that)
Vector
SubVectorRef. add(double c)
Vector
Vector. add(double c)
Add a constant to all entries in this vector.Vector
Vector. add(Vector that)
\(this + that\)Vector
CombinedVectorByRef. deepCopy()
Vector
SubVectorRef. deepCopy()
Vector
Vector. deepCopy()
Vector
ImmutableVector. divide(Vector that)
Vector
Vector. divide(Vector that)
Dividethis
bythat
, entry-by-entry.Vector
ImmutableVector. minus(double scalar)
Vector
ImmutableVector. minus(Vector that)
Vector
SubVectorRef. minus(double c)
Vector
Vector. minus(double c)
Subtract a constant from all entries in this vector.Vector
Vector. minus(Vector that)
\(this - that\)Vector
ImmutableVector. multiply(Vector that)
Vector
Vector. multiply(Vector that)
Multiplythis
bythat
, entry-by-entry.Vector
ImmutableVector. opposite()
Vector
SubVectorRef. opposite()
Vector
Vector. opposite()
Get the opposite of this vector.Vector
ImmutableVector. pow(double scalar)
Vector
SubVectorRef. pow(double c)
Vector
Vector. pow(double c)
Take the exponentiation of all entries in this vector, entry-by-entry.Vector
ImmutableVector. scaled(double scalar)
Vector
ImmutableVector. scaled(Real scalar)
Vector
SubVectorRef. scaled(double c)
Vector
SubVectorRef. scaled(Real c)
Vector
Vector. scaled(double c)
Scale this vector by a constant, entry-by-entry.Vector
Vector. scaled(Real c)
Scale this vector by a constant, entry-by-entry.Vector
ImmutableVector. ZERO()
Vector
Vector. ZERO()
Get a 0-vector that has the same length as this vector.Methods in dev.nm.algebra.linear.vector.doubles with parameters of type Vector Modifier and Type Method Description Vector
ImmutableVector. add(Vector that)
Vector
Vector. add(Vector that)
\(this + that\)double
ImmutableVector. angle(Vector that)
double
Vector. angle(Vector that)
Measure the angle, \(\theta\), betweenthis
andthat
.Vector
ImmutableVector. divide(Vector that)
Vector
Vector. divide(Vector that)
Dividethis
bythat
, entry-by-entry.double
ImmutableVector. innerProduct(Vector that)
double
SubVectorRef. innerProduct(Vector that)
double
Vector. innerProduct(Vector that)
Inner product in the Euclidean space is the dot product.Vector
ImmutableVector. minus(Vector that)
Vector
Vector. minus(Vector that)
\(this - that\)Vector
ImmutableVector. multiply(Vector that)
Vector
Vector. multiply(Vector that)
Multiplythis
bythat
, entry-by-entry.Constructors in dev.nm.algebra.linear.vector.doubles with parameters of type Vector Constructor Description CombinedVectorByRef(Vector v1, Vector v2, Vector... others)
ImmutableVector(Vector v)
Construct a read-only version of a vector.SubVectorRef(Vector v, int from, int to)
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Uses of Vector in dev.nm.algebra.linear.vector.doubles.dense
Classes in dev.nm.algebra.linear.vector.doubles.dense that implement Vector Modifier and Type Class Description class
DenseVector
This class implements the standard, dense,double
based vector representation.Methods in dev.nm.algebra.linear.vector.doubles.dense that return Vector Modifier and Type Method Description static Vector
VectorMathOperation. abs(Vector v)
Computes the absolute values of a vector, element-by-element.Vector
DenseVector. add(Vector that)
static Vector
VectorMathOperation. cos(Vector v)
Computes the cosine of a vector, element-by-element.Vector
DenseVector. divide(Vector that)
static Vector
VectorMathOperation. exp(Vector v)
Computes the exponential of a vector, element-by-element.static Vector
VectorMathOperation. log(Vector v)
Computes the log of a vector, element-by-element.Vector
DenseVector. minus(Vector that)
Vector
DenseVector. multiply(Vector that)
static Vector
VectorMathOperation. rbinom(int n, int nTrials, Vector p)
Generatesn
random binomial numbers.static Vector
VectorMathOperation. rbinom(int n, int nTrials, Vector p, RandomLongGenerator uniform)
Generatesn
random binomial numbers.static Vector
VectorMathOperation. rnorm(int n)
Generatesn
random standard Normals.static Vector
VectorMathOperation. rnorm(int n, RandomStandardNormalGenerator z)
Generatesn
random standard Normals.static Vector
VectorMathOperation. sin(Vector v)
Computes the sine of a vector, element-by-element.static Vector
VectorMathOperation. sqrt(Vector v)
Computes the square roots of a vector, element-by-element.static Vector
VectorMathOperation. squared(Vector v)
Computes the squares of a vector, element-by-element.Methods in dev.nm.algebra.linear.vector.doubles.dense with parameters of type Vector Modifier and Type Method Description static Vector
VectorMathOperation. abs(Vector v)
Computes the absolute values of a vector, element-by-element.Vector
DenseVector. add(Vector that)
DenseVector
VectorMathOperation. add(Vector v1, double c)
Adds a constant to a vector, element-by-element.DenseVector
VectorMathOperation. add(Vector v1, Vector v2)
Adds two vectors, element-by-element.double
DenseVector. angle(Vector that)
double
VectorMathOperation. angle(Vector v1, Vector v2)
Computes the angle between two vectors.static Vector
VectorMathOperation. cos(Vector v)
Computes the cosine of a vector, element-by-element.Vector
DenseVector. divide(Vector that)
DenseVector
VectorMathOperation. divide(Vector v1, Vector v2)
A vector is divided by another vector, element-by-element.static Vector
VectorMathOperation. exp(Vector v)
Computes the exponential of a vector, element-by-element.double
DenseVector. innerProduct(Vector that)
double
VectorMathOperation. innerProduct(Vector v1, Vector v2)
Computes the inner or dot product of two vectors.static Vector
VectorMathOperation. log(Vector v)
Computes the log of a vector, element-by-element.Vector
DenseVector. minus(Vector that)
DenseVector
VectorMathOperation. minus(Vector v1, double c)
Subtracts a constant from a vector, element-by-element.DenseVector
VectorMathOperation. minus(Vector v1, Vector v2)
A vector subtracts another vector, element-by-element.Vector
DenseVector. multiply(Vector that)
DenseVector
VectorMathOperation. multiply(Vector v1, Vector v2)
Multiplies two vectors, element-by-element.double
VectorMathOperation. norm(Vector v1)
Computes the norm of a vector.double
VectorMathOperation. norm(Vector v1, double p)
Computes the norm of a vector.DenseVector
VectorMathOperation. opposite(Vector v1)
Multiples a vector by -1, element-by-element.DenseVector
VectorMathOperation. pow(Vector v1, double c)
Takes a power of a vector, element-by-element.static Vector
VectorMathOperation. rbinom(int n, int nTrials, Vector p)
Generatesn
random binomial numbers.static Vector
VectorMathOperation. rbinom(int n, int nTrials, Vector p, RandomLongGenerator uniform)
Generatesn
random binomial numbers.DenseVector
VectorMathOperation. scaled(Vector v1, double c)
Scales a vector, element-by-element.DenseVector
VectorMathOperation. scaled(Vector v1, Real c)
Scales a vector, element-by-element.static Vector
VectorMathOperation. sin(Vector v)
Computes the sine of a vector, element-by-element.static Vector
VectorMathOperation. sqrt(Vector v)
Computes the square roots of a vector, element-by-element.static Vector
VectorMathOperation. squared(Vector v)
Computes the squares of a vector, element-by-element.static double
VectorMathOperation. sum(Vector v)
Computes the sum of all vector elements.Constructors in dev.nm.algebra.linear.vector.doubles.dense with parameters of type Vector Constructor Description DenseVector(Vector v)
Casts any vector to aDenseVector
. -
Uses of Vector in dev.nm.algebra.linear.vector.doubles.operation
Classes in dev.nm.algebra.linear.vector.doubles.operation that implement Vector Modifier and Type Class Description class
Basis
A basis is a set of linearly independent vectors spanning a vector space.Methods in dev.nm.algebra.linear.vector.doubles.operation that return Vector Modifier and Type Method Description static Vector
VectorFactory. concat(Vector... vectors)
Concatenates an array of vectors into one vector.static Vector
VectorFactory. concat(Collection<Vector> vectors)
Concatenates an array of vectors into one vector.static Vector[]
VectorFactory. cumsum(Vector[] arr)
Gets the cumulative sums.static Vector
VectorFactory. diagonal(Matrix A)
Gets the diagonal of a matrix as a vector.static Vector
VectorFactory. foreach(Vector vector, UnivariateRealFunction f)
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.static Vector
VectorFactory. foreach(Vector vector, DoubleUnaryOperator f)
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.static Vector
VectorFactory. foreachColumn(Matrix matrix, RealScalarFunction f)
Constructs a vector in which each entry is the result of applying aRealScalarFunction
to each column of an input matrix.static Vector
VectorFactory. foreachRow(Matrix matrix, RealScalarFunction f)
Constructs a vector in which each entry is the result of applying aRealScalarFunction
to each row of an input matrix.static Vector
VectorFactory. foreachVector(Vector[] vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector.static Vector[]
VectorFactory. foreachVector(Vector[] vectors, RealVectorFunction f)
Applies a real vector function on each input vector.static Vector
VectorFactory. foreachVector(Collection<Vector> vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector.static Vector[]
VectorFactory. foreachVector(Collection<Vector> vectors, RealVectorFunction f)
Applies a real vector function on each input vector.static Vector[]
VectorFactory. get0s(int dimension, int n)
Getsn
0 vectors.static Vector
VectorFactory. getCoordinate(Vector[] vectors, int i)
Gets the vector entries from a particular coordinate.static Vector
VectorFactory. getCoordinate(Collection<Vector> vectors, int i)
Gets the vector entries from a particular coordinate.Vector
RealVectorSpace. getLinearSpan(double... d)
Deprecated.Not supported yet.Vector
RealVectorSpace. getSpanningCoefficients(Vector b)
Find a linear combination of the basis that best approximates a vector in the least square sense.static Vector
VectorFactory. replaceInPlace(Vector original, int from, Vector replacement)
Replaces a sub-vector with a given smaller vector.static Vector
VectorFactory. subDiagonal(Matrix A)
Gets the sub-diagonal of a matrix as a vector.static Vector
VectorFactory. subVector(Vector vector, int[] indices)
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).static Vector
VectorFactory. subVector(Vector vector, int from, int to)
Gets a sub-vector from a vector.static Vector
VectorFactory. subVector(Vector vector, List<Integer> indices)
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).static Vector
VectorFactory. superDiagonal(Matrix A)
Gets the super-diagonal of a matrix as a vector.Methods in dev.nm.algebra.linear.vector.doubles.operation that return types with arguments of type Vector Modifier and Type Method Description static List<Vector>
Basis. getBasis(int dim)
Get the full set of the standard basis vectors.static List<Vector>
Basis. getBasis(int dim, int nCols)
Get a subset of the standard basis vectors.List<Vector>
RealVectorSpace. getBasis()
Get the orthogonal basis.List<Vector>
RealVectorSpace. getComplement()
Get the basis of the orthogonal complement.static List<Vector>
VectorFactory. getOffsetVectors(Vector v0, Vector dv, int a, int b)
Given the reference vectorv0
, the deltadv
, and the range[a, b]
, the offset vectors are: v0 + a * dv, v0 + (a + 1) * dv, ..., v0 + b * dv.Methods in dev.nm.algebra.linear.vector.doubles.operation with parameters of type Vector Modifier and Type Method Description static Vector
VectorFactory. concat(Vector... vectors)
Concatenates an array of vectors into one vector.static Vector[]
VectorFactory. cumsum(Vector[] arr)
Gets the cumulative sums.static Vector
VectorFactory. foreach(Vector vector, UnivariateRealFunction f)
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.static Vector
VectorFactory. foreach(Vector vector, DoubleUnaryOperator f)
Constructs a new vector in which each entry is the result of applying a function to the corresponding entry of a vector.static Vector
VectorFactory. foreachVector(Vector[] vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector.static Vector[]
VectorFactory. foreachVector(Vector[] vectors, RealVectorFunction f)
Applies a real vector function on each input vector.static Vector
VectorFactory. getCoordinate(Vector[] vectors, int i)
Gets the vector entries from a particular coordinate.static List<Vector>
VectorFactory. getOffsetVectors(Vector v0, Vector dv, int a, int b)
Given the reference vectorv0
, the deltadv
, and the range[a, b]
, the offset vectors are: v0 + a * dv, v0 + (a + 1) * dv, ..., v0 + b * dv.Vector
RealVectorSpace. getSpanningCoefficients(Vector b)
Find a linear combination of the basis that best approximates a vector in the least square sense.boolean
RealVectorSpace. isInKernel(Vector b)
Deprecated.Not supported yet.boolean
RealVectorSpace. isSpanned(Vector b)
Check whether a vector is in the span of the basis.static Vector
VectorFactory. replaceInPlace(Vector original, int from, Vector replacement)
Replaces a sub-vector with a given smaller vector.static Vector
VectorFactory. subVector(Vector vector, int[] indices)
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).static Vector
VectorFactory. subVector(Vector vector, int from, int to)
Gets a sub-vector from a vector.static Vector
VectorFactory. subVector(Vector vector, List<Integer> indices)
Gets a sub-vector from a vector according to a given array of ordered indices (repetition allowed).Method parameters in dev.nm.algebra.linear.vector.doubles.operation with type arguments of type Vector Modifier and Type Method Description static Vector
VectorFactory. concat(Collection<Vector> vectors)
Concatenates an array of vectors into one vector.static Vector
VectorFactory. foreachVector(Collection<Vector> vectors, RealScalarFunction f)
Applies aRealScalarFunction
on each input vector.static Vector[]
VectorFactory. foreachVector(Collection<Vector> vectors, RealVectorFunction f)
Applies a real vector function on each input vector.static Vector
VectorFactory. getCoordinate(Collection<Vector> vectors, int i)
Gets the vector entries from a particular coordinate.Constructors in dev.nm.algebra.linear.vector.doubles.operation with parameters of type Vector Constructor Description Projection(Vector v, Vector w)
Project a vector v onto another vector.Projection(Vector v, Vector[] basis)
Project a vector v onto a set of basis {wi}.Projection(Vector v, List<Vector> basis)
Project a vector v onto a set of basis {wi}.RealVectorSpace(double epsilon, Vector... elements)
Construct a vector space from an array of vectors.RealVectorSpace(Vector... elements)
Construct a vector space from an array of vectors.Constructor parameters in dev.nm.algebra.linear.vector.doubles.operation with type arguments of type Vector Constructor Description Projection(Vector v, List<Vector> basis)
Project a vector v onto a set of basis {wi}.RealVectorSpace(List<Vector> elements)
Construct a vector space from a list of vectors.RealVectorSpace(List<Vector> elements, double epsilon)
Construct a vector space from a list of vectors. -
Uses of Vector in dev.nm.analysis.differentialequation.ode.ivp.problem
Methods in dev.nm.analysis.differentialequation.ode.ivp.problem that return Vector Modifier and Type Method Description Vector
DerivativeFunction. evaluate(double x, Vector y)
Computes the derivative at the given point, x.Vector
ODE1stOrder. y0()
Gets the initial value of y, that is, y0.Methods in dev.nm.analysis.differentialequation.ode.ivp.problem with parameters of type Vector Modifier and Type Method Description Vector
DerivativeFunction. evaluate(double x, Vector y)
Computes the derivative at the given point, x.Constructors in dev.nm.analysis.differentialequation.ode.ivp.problem with parameters of type Vector Constructor Description ODE1stOrder(DerivativeFunction dy, Vector y0, double x0, double x1)
Constructs a first order ODE with the given vector-valued function and its initial values.ODE1stOrder(RealVectorFunction F, Vector y0, double x0, double x1)
Constructs a first order ODE with the given vector-valued function and its initial values.ODE1stOrderWith2ndDerivative(DerivativeFunction dy, DerivativeFunction ddy, Vector y0, double x0, double x1)
Constructs a first order ODE with initial values.ODE1stOrderWith2ndDerivative(RealVectorFunction dy, RealVectorFunction ddy, Vector y0, double x0, double x1)
Constructs a first order ODE with initial values. -
Uses of Vector in dev.nm.analysis.differentialequation.ode.ivp.solver
Methods in dev.nm.analysis.differentialequation.ode.ivp.solver that return Vector Modifier and Type Method Description Vector[]
ODEIntegrator. integrate(ODE1stOrder ode, double[] x)
This is the integration method that approximates the solution of a first order ODE.Vector[]
ODESolution. y()
Get the corresponding values of the dependent variable.Constructors in dev.nm.analysis.differentialequation.ode.ivp.solver with parameters of type Vector Constructor Description ODESolution(double[] x, Vector[] y)
Create a solution with estimated values at the given points. -
Uses of Vector in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton
Methods in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton that return Vector Modifier and Type Method Description Vector
ABMPredictorCorrector. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector1. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector2. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector3. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector4. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector5. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector1. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector2. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector3. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector4. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector5. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Methods in dev.nm.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton with parameters of type Vector Modifier and Type Method Description Vector
ABMPredictorCorrector. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector1. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector2. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector3. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector4. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector5. correct(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector1. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector2. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector3. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector4. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
Vector
ABMPredictorCorrector5. predict(DerivativeFunction dy, double h, double[] x, Vector[] y)
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Uses of Vector in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta
Methods in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta that return Vector Modifier and Type Method Description Vector[]
RungeKuttaIntegrator. integrate(ODE1stOrder ode, double[] x)
Vector
RungeKutta1. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta10. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta2. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta3. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta4. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta5. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta6. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta7. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta8. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKuttaStepper. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Methods in dev.nm.analysis.differentialequation.ode.ivp.solver.rungekutta with parameters of type Vector Modifier and Type Method Description Vector
RungeKutta1. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta10. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta2. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta3. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta4. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta5. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta6. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta7. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKutta8. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
Vector
RungeKuttaStepper. step(DerivativeFunction dy, Vector y0, double x0, double x1, double h)
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Uses of Vector in dev.nm.analysis.differentialequation.pde.finitedifference
Fields in dev.nm.analysis.differentialequation.pde.finitedifference declared as Vector Modifier and Type Field Description protected Vector[]
PDETimeSpaceGrid1D. u
the solution matrixMethods in dev.nm.analysis.differentialequation.pde.finitedifference that return Vector Modifier and Type Method Description abstract Vector
PDETimeSpaceGrid1D. getRHS(int m)
Vector[]
PDETimeSpaceGrid1D. propagate()
Propagates the grid to the next time step by solving \(Au=d\). -
Uses of Vector in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
Methods in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation that return Vector Modifier and Type Method Description Vector
CrankNicolsonConvectionDiffusionEquation1D.Coefficients. getRHS(Vector um, double tm)
Computes the right hand side vector of the Crank-Nicolson scheme.Methods in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation with parameters of type Vector Modifier and Type Method Description Vector
CrankNicolsonConvectionDiffusionEquation1D.Coefficients. getRHS(Vector um, double tm)
Computes the right hand side vector of the Crank-Nicolson scheme. -
Uses of Vector in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation
Methods in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation that return Vector Modifier and Type Method Description Vector
CrankNicolsonHeatEquation1D.Coefficients. getRHS(Vector um, double tm, double tmp1)
Methods in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation with parameters of type Vector Modifier and Type Method Description Vector
CrankNicolsonHeatEquation1D.Coefficients. getRHS(Vector um, double tm, double tmp1)
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Uses of Vector in dev.nm.analysis.differentiation
Methods in dev.nm.analysis.differentiation with parameters of type Vector Modifier and Type Method Description Double
Ridders. evaluate(Vector x)
Evaluate the function f at x, where x is from the domain.double
Ridders. evaluate(Vector x, double h)
Evaluate numerically the derivative of f at point x, f'(x), with step size h. -
Uses of Vector in dev.nm.analysis.differentiation.multivariate
Classes in dev.nm.analysis.differentiation.multivariate that implement Vector Modifier and Type Class Description class
Gradient
The gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field, and of which the magnitude is the greatest rate of change.Methods in dev.nm.analysis.differentiation.multivariate that return Vector Modifier and Type Method Description Vector
GradientFunction. evaluate(Vector x)
Methods in dev.nm.analysis.differentiation.multivariate with parameters of type Vector Modifier and Type Method Description Vector
GradientFunction. evaluate(Vector x)
Matrix
HessianFunction. evaluate(Vector x)
Matrix
JacobianFunction. evaluate(Vector x)
Double
MultivariateFiniteDifference. evaluate(Vector x)
Evaluate numerically the partial derivative of f at point x.double
MultivariateFiniteDifference. evaluate(Vector x, double h)
Evaluate numerically the partial derivative of f at point x with step size h.Constructors in dev.nm.analysis.differentiation.multivariate with parameters of type Vector Constructor Description BorderedHessian(RealScalarFunction f, RealScalarFunction g, Vector x)
Construct the bordered Hessian matrix for multivariate functions f and g at point x.Gradient(RealScalarFunction f, Vector x)
Construct the gradient vector for a multivariate function f at point x.Hessian(RealScalarFunction f, Vector x)
Construct the Hessian matrix for a multivariate function f at point x.Jacobian(RealScalarFunction[] f, Vector x)
Construct the Jacobian matrix for a multivariate function f at point x.Jacobian(RealVectorFunction f, Vector x)
Construct the Jacobian matrix for a multivariate function f at point x.Jacobian(List<RealScalarFunction> f, Vector x)
Construct the Jacobian matrix for a multivariate function f at point x. -
Uses of Vector in dev.nm.analysis.function
Fields in dev.nm.analysis.function with type parameters of type Vector Modifier and Type Field Description protected Function<Vector,R>
SubFunction. f
the original, unrestricted functionMethods in dev.nm.analysis.function that return Vector Modifier and Type Method Description static Vector
SubFunction. getAllParts(Vector variables, Map<Integer,Double> fixing)
Combines the variable and fixed values to form an input to the original function.Methods in dev.nm.analysis.function with parameters of type Vector Modifier and Type Method Description static Vector
SubFunction. getAllParts(Vector variables, Map<Integer,Double> fixing)
Combines the variable and fixed values to form an input to the original function.Constructor parameters in dev.nm.analysis.function with type arguments of type Vector Constructor Description SubFunction(Function<Vector,R> f, Map<Integer,Double> fixing)
Constructs a sub-function. -
Uses of Vector in dev.nm.analysis.function.matrix
Methods in dev.nm.analysis.function.matrix with parameters of type Vector Modifier and Type Method Description Matrix
R1toMatrix. evaluate(Vector x)
Matrix
R2toMatrix. evaluate(Vector x)
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Uses of Vector in dev.nm.analysis.function.rn2r1
Methods in dev.nm.analysis.function.rn2r1 with parameters of type Vector Modifier and Type Method Description Double
AbstractBivariateRealFunction. evaluate(Vector x)
Double
AbstractTrivariateRealFunction. evaluate(Vector x)
Double
QuadraticFunction. evaluate(Vector z)
Double
R1Projection. evaluate(Vector x)
Double
RealScalarSubFunction. evaluate(Vector x)
Constructors in dev.nm.analysis.function.rn2r1 with parameters of type Vector Constructor Description QuadraticFunction(Matrix H, Vector p)
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p\).QuadraticFunction(Matrix H, Vector p, double c)
Construct a quadratic function of this form: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\). -
Uses of Vector in dev.nm.analysis.function.rn2r1.univariate
Methods in dev.nm.analysis.function.rn2r1.univariate with parameters of type Vector Modifier and Type Method Description Double
AbstractUnivariateRealFunction. evaluate(Vector x)
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Uses of Vector in dev.nm.analysis.function.rn2rm
Methods in dev.nm.analysis.function.rn2rm that return Vector Modifier and Type Method Description abstract Vector
AbstractR1RnFunction. evaluate(double x)
Vector
AbstractR1RnFunction. evaluate(Vector x)
Vector
RealVectorSubFunction. evaluate(Vector x)
Methods in dev.nm.analysis.function.rn2rm with parameters of type Vector Modifier and Type Method Description Vector
AbstractR1RnFunction. evaluate(Vector x)
Vector
RealVectorSubFunction. evaluate(Vector x)
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Uses of Vector in dev.nm.analysis.function.special
Methods in dev.nm.analysis.function.special with parameters of type Vector Modifier and Type Method Description Double
Rastrigin. evaluate(Vector x)
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Uses of Vector in dev.nm.analysis.function.special.beta
Methods in dev.nm.analysis.function.special.beta with parameters of type Vector Modifier and Type Method Description Double
MultinomialBetaFunction. evaluate(Vector a)
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Uses of Vector in dev.nm.analysis.root.multivariate
Methods in dev.nm.analysis.root.multivariate that return Vector Modifier and Type Method Description Vector
NewtonSystemRoot. solve(RealScalarFunction[] f, Vector guess)
Searches for a root, x such that f(x) = 0.Vector
NewtonSystemRoot. solve(RealVectorFunction f, Vector guess)
Searches for a root, x such that f(x) = 0.Methods in dev.nm.analysis.root.multivariate with parameters of type Vector Modifier and Type Method Description Vector
NewtonSystemRoot. solve(RealScalarFunction[] f, Vector guess)
Searches for a root, x such that f(x) = 0.Vector
NewtonSystemRoot. solve(RealVectorFunction f, Vector guess)
Searches for a root, x such that f(x) = 0. -
Uses of Vector in dev.nm.geometry
Constructors in dev.nm.geometry with parameters of type Vector Constructor Description Point(Vector coordinates)
Create a point with given coordinates. -
Uses of Vector in dev.nm.misc.algorithm.iterative.monitor
Methods in dev.nm.misc.algorithm.iterative.monitor with parameters of type Vector Modifier and Type Method Description void
VectorMonitor. addIterate(Vector x)
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Uses of Vector in dev.nm.misc.algorithm.stopcondition
Methods in dev.nm.misc.algorithm.stopcondition with parameters of type Vector Modifier and Type Method Description double
AfterNoImprovement. energy(Vector x, double... information)
boolean
AfterIterations. isStopped(Vector x, double... information)
boolean
AfterNoImprovement. isStopped(Vector x, double... information)
boolean
AndStopConditions. isStopped(Vector x, double... information)
boolean
AtThreshold. isStopped(Vector x, double... information)
boolean
OrStopConditions. isStopped(Vector x, double... information)
boolean
StopCondition. isStopped(Vector x, double... information)
This is called after each iteration to determine whether the termination conditions are met, e.g., convergence. -
Uses of Vector in dev.nm.misc.datastructure
Methods in dev.nm.misc.datastructure that return Vector Modifier and Type Method Description Vector
FlexibleTable. getColumn(int col)
Vector
FlexibleTable. getRow(int row)
Methods in dev.nm.misc.datastructure that return types with arguments of type Vector Modifier and Type Method Description Iterator<Vector>
MultiDimensionalGrid. iterator()
Methods in dev.nm.misc.datastructure with parameters of type Vector Modifier and Type Method Description static void
DimensionCheck. throwIfIncompatible4Multiplication(Table A, Vector v)
Throws ifA.nCols() != v.size()
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Uses of Vector in dev.nm.solver.multivariate.constrained
Classes in dev.nm.solver.multivariate.constrained with type parameters of type Vector Modifier and Type Interface Description static interface
SubProblemMinimizer.ConstrainedMinimizerFactory<U extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeSolution<Vector>>>
This factory constructs a new instance of ConstrainedMinimizer to solve a real valued minimization problem.Methods in dev.nm.solver.multivariate.constrained that return types with arguments of type Vector Modifier and Type Method Description SubProblemMinimizer.IterativeSolution<Vector>
SubProblemMinimizer. solve(ConstrainedOptimSubProblem problem)
IterativeSolution<Vector>
SubProblemMinimizer. solve(ConstrainedOptimProblem subProblem)
Solves a constrained optimization sub-problem that is already in the form of a ConstrainedOptimProblem.SubProblemMinimizer.IterativeSolution<Vector>
SubProblemMinimizer. solve(ConstrainedOptimProblem problem, Map<Integer,Double> fixing)
Solves a constrained sub-problem by specifying the fixing explicitly.Constructor parameters in dev.nm.solver.multivariate.constrained with type arguments of type Vector Constructor Description SubProblemMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeSolution<Vector>>> factory)
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Uses of Vector in dev.nm.solver.multivariate.constrained.constraint
Methods in dev.nm.solver.multivariate.constrained.constraint that return Vector Modifier and Type Method Description static Vector
ConstraintsUtils. evaluate(Constraints constraints, Vector x)
Evaluates the constraints.Methods in dev.nm.solver.multivariate.constrained.constraint with parameters of type Vector Modifier and Type Method Description static Vector
ConstraintsUtils. evaluate(Constraints constraints, Vector x)
Evaluates the constraints.static boolean
ConstraintsUtils. isSatisfied(Constraints constraints, Vector x)
Checks if the constraints are satisfied.static boolean
ConstraintsUtils. isSatisfied(Constraints constraints, Vector x, double epsilon)
Checks if the constraints are satisfied. -
Uses of Vector in dev.nm.solver.multivariate.constrained.constraint.linear
Methods in dev.nm.solver.multivariate.constrained.constraint.linear that return Vector Modifier and Type Method Description Vector
LinearGreaterThanConstraints. getFeasibleInitialPoint()
Given a collection of linear greater-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints.Vector
LinearGreaterThanConstraints. getFeasibleInitialPoint(LinearEqualityConstraints equal)
Given a collection of linear greater-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints.Vector
LinearLessThanConstraints. getFeasibleInitialPoint()
Given a collection of linear less-than-or-equal-to constraints, find a feasible initial point that satisfy the constraints.Vector
LinearLessThanConstraints. getFeasibleInitialPoint(LinearEqualityConstraints equal)
Given a collection of linear less-than-or-equal-to constraints as well as a collection of equality constraints, find a feasible initial point that satisfy the constraints.Vector
BoxConstraints. lowerBounds()
Gets the lower bounds.Vector
BoxConstraints. upperBounds()
Gets the upper bounds.Methods in dev.nm.solver.multivariate.constrained.constraint.linear with parameters of type Vector Modifier and Type Method Description Matrix
LinearConstraints. getActiveConstraints(Vector x, double epsilon)
Get the active constraint.ArrayList<Integer>
LinearConstraints. getActiveRows(Vector x, double epsilon)
Get the active constraint indices.static boolean
BoxConstraints. isInBox(Vector x, Vector lower, Vector upper)
Check if a solution is within a box.Constructors in dev.nm.solver.multivariate.constrained.constraint.linear with parameters of type Vector Constructor Description BoxConstraints(Vector lower, Vector upper)
Construct a set of bound constraints.LinearConstraints(Matrix A, Vector b)
Construct a collection of linear constraints.LinearEqualityConstraints(Matrix A, Vector b)
Construct a collection of linear equality constraints.LinearGreaterThanConstraints(Matrix A, Vector b)
Construct a collection of linear greater-than or equal-to constraints.LinearLessThanConstraints(Matrix A, Vector b)
Construct a collection of linear less-than or equal-to constraints. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing with parameters of type Vector Constructor Description CentralPath(Matrix X, Vector y, Matrix S)
Construct a central path. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.problem
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.problem with parameters of type Vector Constructor Description EqualityConstraints(Vector b, SymmetricMatrix C, SymmetricMatrix[] A)
Construct the equality constraints for a dual SDP problem, \(\sum_{i=1}^{p}y_i\mathbf{A_i}+\textbf{S} = \textbf{C}, \textbf{S} \succeq \textbf{0}\).SDPDualProblem(Vector b, SymmetricMatrix C, SymmetricMatrix[] A)
Constructs a dual SDP problem. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint with parameters of type Vector Modifier and Type Method Description PrimalDualSolution
AntoniouLu2007. iterate(PrimalDualSolution soln0, Vector Ax, Vector Aty, double mu)
PrimalDualSolution
SDPT3v4_1a. iterate(PrimalDualSolution soln0, Vector Ax, Vector Aty, double mu)
PrimalDualSolution
SDPT3v4_1b. iterate(PrimalDualSolution soln0, Vector Ax, Vector Aty, double mu)
PrimalDualSolution
SDPT3v4. iterate(PrimalDualSolution soln0, Vector Ax, Vector Aty, double mu)
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.interiorpoint with parameters of type Vector Constructor Description PrimalDualSolution(Vector x, Vector s, Vector y)
Construct a solution to a primal and a dual SOCP problems. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem that return Vector Modifier and Type Method Description Vector
SOCPDualProblem. b()
Gets b.Vector
SOCPDualProblem1. b()
Gets b.Vector
SOCPGeneralConstraint. b()
Gets b.Vector
SOCPDualProblem. c()
Vector
SOCPDualProblem. c(int i)
Gets ci.Vector
SOCPGeneralConstraint. c()
Gets c.Vector
SOCPLinearEquality. c()
Vector
SOCPLinearInequality. c()
Vector
SOCPDualProblem1. c_full()
Vector
SOCPDualProblem1. c_l()
Vector
SOCPDualProblem1. c_l_full()
Vector
SOCPDualProblem1. c_q(int i)
Gets \(c^q_i\).Vector
SOCPDualProblem1. c_q_full()
Vector
SOCPDualProblem1. c_u()
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem with parameters of type Vector Constructor Description EqualityConstraints(Vector b, Matrix[] A, Vector[] c)
Constructs the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ..., q\).EqualityConstraints(Vector b, Matrix[] A, Vector[] c)
Constructs the equality constraints for a dual SOCP problem, \[ \max_y \mathbf{b'y} \textrm{ s.t.,} \\ \mathbf{{A^q}_i'y + s_i = c^q_i}, s_i \in K_i, i = 1, 2, ..., q \\ \mathbf{{A^{\ell}}^T y + z^{\ell} = c^{\ell}} \\ \mathbf{{A^{u}}^T y = c^{u}} \\ \]SOCPDualProblem(Vector b, Matrix[] A, Vector[] c)
Constructs a dual SOCP problem.SOCPDualProblem1(Vector b, Matrix[] A_q, Vector[] c_q)
Constructs a dual SOCP problem.SOCPDualProblem1(Vector b, Matrix[] A_q, Vector[] c_q, Matrix A_l, Vector c_l, Matrix A_u, Vector c_u)
Constructs a dual SOCP problem.SOCPGeneralConstraint(Matrix A, Vector c, Vector b, double d)
Constructs a SOCP general constraint.SOCPGeneralProblem(Vector f, SOCPGeneralConstraint[] constraints)
Construct a general Second Order Conic Programming problem.SOCPGeneralProblem(Vector f, List<SOCPGeneralConstraint> constraints)
Construct a general Second Order Conic Programming problem.SOCPGeneralProblem1(Vector f, SOCPGeneralConstraint[] generalConstraints, SOCPLinearInequality[] linearInequalities, SOCPLinearEquality[] linearEqualities)
Construct a general Second Order Conic Programming problem.\[ \begin{align*} \min_x \quad &; \mathbf{f^T} x \\ \textrm{s.t.} \quad &; \lVert {A_i}^T x + c_i \rVert_2 \leq b_i^T x + d_i,\quad i = 1,\dots,m \end{align*} \]SOCPGeneralProblem1(Vector f, List<SOCPGeneralConstraint> generalConstraints)
Construct a general Second Order Conic Programming problem.SOCPGeneralProblem1(Vector f, List<SOCPGeneralConstraint> generalConstraints, List<SOCPLinearInequality> linearInequalities, List<SOCPLinearEquality> linearEqualities)
Construct a general Second Order Conic Programming problem.SOCPLinearEquality(Matrix A, Vector c)
SOCPLinearInequality(Matrix A, Vector c)
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Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization that return Vector Modifier and Type Method Description Vector
SOCPPortfolioObjectiveFunction. b()
Gets the objective vector, b, in the compact form.Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization with parameters of type Vector Modifier and Type Method Description boolean
MarketImpact1. areAllConstraintsSatisfied(Vector y)
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.boolean
PortfolioRiskExactSigma. areAllConstraintsSatisfied(Vector y)
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.abstract boolean
SOCPPortfolioConstraint. areAllConstraintsSatisfied(Vector y)
Checks whether all SOCP constraints represented by this portfolio constraint are satisfied.boolean
SOCPPortfolioProblem. areAllConstraintsSatisfied(Vector x)
Checks whether the constraints are satisfied with a solution vector x.boolean
SOCPPortfolioProblem1. areAllConstraintsSatisfied(Vector x)
Checks whether the constraints are satisfied with a solution vector x.Double
MarketImpact1. evaluate(Vector y)
Double
PortfolioRiskExactSigma. evaluate(Vector y)
Double
SOCPPortfolioObjectiveFunction. evaluate(Vector y)
Computes the final objective function value.Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization with parameters of type Vector Constructor Description MarketImpact1(Vector w_0, Vector m)
Constructs a market impact term.SOCPPortfolioObjectiveFunction(Vector r_bar, double[] lambda, SOCPRiskConstraint risk, SOCPPortfolioConstraint impact)
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.SOCPPortfolioObjectiveFunction(Vector r_bar, double lambda, SOCPRiskConstraint risk)
Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.problem with parameters of type Vector Constructor Description LPCanonicalProblem1(Vector c, Matrix A, Vector b)
Construct a linear programming problem in the canonical form.LPCanonicalProblem1(Vector cost, LinearGreaterThanConstraints greater)
Construct a linear programming problem in the canonical form.LPCanonicalProblem2(Vector c, Matrix A, Vector b)
Construct a linear programming problem in the canonical form.LPCanonicalProblem2(Vector cost, LinearLessThanConstraints less)
Construct a linear programming problem in the canonical form.LPProblemImpl1(Vector cost, LinearGreaterThanConstraints greater, LinearEqualityConstraints equal)
Construct a general linear programming problem with only greater-than-or-equal-to and equality constraints.LPProblemImpl1(Vector cost, LinearGreaterThanConstraints greater, LinearLessThanConstraints less, LinearEqualityConstraints equal, BoxConstraints bounds)
Construct a general linear programming problem.LPStandardProblem(Vector c, LinearEqualityConstraints equal)
Construct a linear programming problem in the standard form. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver that return Vector Modifier and Type Method Description Vector
LPRevisedSimplexSolver. findFeasiblePoint(Matrix A, Vector b)
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver with parameters of type Vector Modifier and Type Method Description Vector
LPRevisedSimplexSolver. findFeasiblePoint(Matrix A, Vector b)
dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver.LPRevisedSimplexSolver.WorkingSet
LPRevisedSimplexSolver. findVertex(Matrix A, Vector b, Vector x0)
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver with parameters of type Vector Constructor Description Problem(Matrix A, Vector b, Vector c)
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Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem with parameters of type Vector Modifier and Type Method Description ImmutableVector
QPProblemOnlyEqualityConstraints. getSolutionToOriginalProblem(Vector phi)
Backs out the solution for the original (constrained) problem, if the modified (unconstrained) problem can be solved. -
Uses of Vector in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.solver.activeset with parameters of type Vector Modifier and Type Method Description QPSolution
QPPrimalActiveSetMinimizer.Solution. search(Vector initial)
Searches for a minimizer for the quadratic programming problem. -
Uses of Vector in dev.nm.solver.multivariate.constrained.general.box
Methods in dev.nm.solver.multivariate.constrained.general.box that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
BoxGeneralizedSimulatedAnnealingMinimizer. solve(BoxOptimProblem problem)
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Uses of Vector in dev.nm.solver.multivariate.constrained.general.penaltymethod
Methods in dev.nm.solver.multivariate.constrained.general.penaltymethod that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
PenaltyMethodMinimizer. solve(ConstrainedOptimProblem problem)
Methods in dev.nm.solver.multivariate.constrained.general.penaltymethod with parameters of type Vector Modifier and Type Method Description Double
AbsoluteErrorPenalty. evaluate(Vector x)
Double
CourantPenalty. evaluate(Vector x)
Double
FletcherPenalty. evaluate(Vector x)
Double
SumOfPenalties. evaluate(Vector x)
Double
ZeroPenalty. evaluate(Vector x)
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Uses of Vector in dev.nm.solver.multivariate.constrained.general.sqp.activeset
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset that return Vector Modifier and Type Method Description Vector
SQPActiveSetMinimizer.Solution. minimizer()
Vector
SQPActiveSetMinimizer.Solution. search(Vector x0)
Search for a solution that minimizes the objective function from the given starting point.Vector
SQPActiveSetMinimizer.Solution. search(Vector... initials)
Search for a solution that optimizes the objective function from the given starting points.Vector
SQPActiveSetMinimizer.Solution. search(Vector x0, Vector lambda0, Vector mu0)
Search for a solution that minimizes the objective function from the given starting point.Vector
SQPActiveSetOnlyInequalityConstraintMinimizer.Solution. search(Vector... initials)
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
SQPActiveSetOnlyInequalityConstraintMinimizer. solve(RealScalarFunction f, RealVectorFunction g, GreaterThanConstraints greater)
Minimize a function subject to only inequality constraints.IterativeSolution<Vector>
SQPActiveSetOnlyInequalityConstraintMinimizer. solve(RealScalarFunction f, GreaterThanConstraints greater)
Minimize a function subject to only inequality constraints.Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset with parameters of type Vector Modifier and Type Method Description double
SQPASVariation. alpha(Vector x, Vector d, Vector v, Vector u)
Get the percentage increment along the minimizer increment direction.double
SQPASVariation1. alpha(Vector x, Vector d, Vector v, Vector u)
Get the percentage increment along the minimizer increment direction.Matrix
SQPASVariation. getInitialHessian(Vector x0, Vector v0, Vector u0)
Get the initial Hessian matrix.Matrix
SQPASVariation1. getInitialHessian(Vector x0, Vector v0, Vector u0)
Vector
SQPActiveSetMinimizer.Solution. search(Vector x0)
Search for a solution that minimizes the objective function from the given starting point.Vector
SQPActiveSetMinimizer.Solution. search(Vector... initials)
Search for a solution that optimizes the objective function from the given starting points.Vector
SQPActiveSetMinimizer.Solution. search(Vector x0, Vector lambda0, Vector mu0)
Search for a solution that minimizes the objective function from the given starting point.Vector
SQPActiveSetOnlyInequalityConstraintMinimizer.Solution. search(Vector... initials)
void
SQPActiveSetMinimizer.Solution. setInitials(Vector... initials)
Supply the starting points for the search.Matrix
SQPASVariation. updateHessian(Vector x1, Vector v1, Vector u1, Vector d, Vector g0, Matrix Ae0, Matrix Ai0, Matrix W0)
Update the Hessian matrix using the latest iterates.Matrix
SQPASVariation1. updateHessian(Vector x1, Vector v1, Vector u1, Vector d, Vector g0, Matrix Ae0, Matrix Ai0, Matrix W0)
Update the Hessian matrix using the latest iterates. -
Uses of Vector in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
SQPActiveSetOnlyEqualityConstraint1Minimizer. solve(RealScalarFunction f, EqualityConstraints equal)
Minimize a function subject to only equality constraints.IterativeSolution<Vector>
SQPActiveSetOnlyEqualityConstraint1Minimizer. solve(ConstrainedOptimProblem problem)
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint with parameters of type Vector Modifier and Type Method Description double
SQPASEVariation. alpha(Vector x, Vector d, Vector u)
Get the percentage increment along the minimizer increment direction.double
SQPASEVariation1. alpha(Vector x, Vector d, Vector v)
Matrix
SQPASEVariation. getInitialHessian(Vector x0, Vector u0)
Get the initial Hessian matrix.Matrix
SQPASEVariation1. getInitialHessian(Vector x0, Vector v0)
Matrix
SQPASEVariation. updateHessian(Vector x1, Vector u1, Vector d, Vector g0, Matrix A0, Matrix W0)
Update the Hessian matrix using the latest iterates.Matrix
SQPASEVariation1. updateHessian(Vector x1, Vector v1, Vector d, Vector g0, Matrix A0, Matrix W0)
Matrix
SQPASEVariation2. updateHessian(Vector x1, Vector v1, Vector d, Vector g0, Matrix A0, Matrix W0)
protected Matrix
SQPASEVariation1. W(Vector x, Vector u)
Compute W. -
Uses of Vector in dev.nm.solver.multivariate.constrained.integer
Classes in dev.nm.solver.multivariate.constrained.integer with type parameters of type Vector Modifier and Type Interface Description interface
IPMinimizer<T extends IPProblem,S extends MinimizationSolution<Vector>>
An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints. -
Uses of Vector in dev.nm.solver.multivariate.constrained.integer.bruteforce
Methods in dev.nm.solver.multivariate.constrained.integer.bruteforce that return Vector Modifier and Type Method Description Vector
BruteForceIPMinimizer.Solution. minimizer()
Methods in dev.nm.solver.multivariate.constrained.integer.bruteforce with parameters of type Vector Modifier and Type Method Description void
BruteForceIPMinimizer.Solution. search(Vector initial)
Constructor parameters in dev.nm.solver.multivariate.constrained.integer.bruteforce with type arguments of type Vector Constructor Description BruteForceIPMinimizer(SubProblemMinimizer.ConstrainedMinimizerFactory<? extends ConstrainedMinimizer<ConstrainedOptimProblem,IterativeSolution<Vector>>> factory)
Constructs a brute force minimizer to solve integral constrained minimization problems. -
Uses of Vector in dev.nm.solver.multivariate.constrained.integer.linear.bb
Methods in dev.nm.solver.multivariate.constrained.integer.linear.bb that return types with arguments of type Vector Modifier and Type Method Description MinimizationSolution<Vector>
ILPBranchAndBoundMinimizer. solve(ILPProblem problem)
Methods in dev.nm.solver.multivariate.constrained.integer.linear.bb with parameters of type Vector Modifier and Type Method Description static LinearGreaterThanConstraints
ILPNode. getGreaterThanConstraint(Vector minimizer, int i)
Construct a greater-than constraint for the branching greater-than subproblem.static LinearLessThanConstraints
ILPNode. getLessThanConstraint(Vector minimizer, int i)
Construct a less-than constraint for the branching less-than subproblem. -
Uses of Vector in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane
Methods in dev.nm.solver.multivariate.constrained.integer.linear.cuttingplane that return types with arguments of type Vector Modifier and Type Method Description MinimizationSolution<Vector>
SimplexCuttingPlaneMinimizer. solve(ILPProblem problem)
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Uses of Vector in dev.nm.solver.multivariate.constrained.integer.linear.problem
Constructors in dev.nm.solver.multivariate.constrained.integer.linear.problem with parameters of type Vector Constructor Description ILPProblemImpl1(Vector cost, LinearGreaterThanConstraints greater, LinearLessThanConstraints less, LinearEqualityConstraints equal, BoxConstraints bounds, int[] integers, double epsilon)
Construct an ILP problem, in which the variables can be real or integral.PureILPProblem(Vector cost, LinearGreaterThanConstraints greater, LinearLessThanConstraints less, LinearEqualityConstraints equal, BoxConstraints bounds, double epsilon)
Construct a pure ILP problem. -
Uses of Vector in dev.nm.solver.multivariate.constrained.problem
Methods in dev.nm.solver.multivariate.constrained.problem that return Vector Modifier and Type Method Description Vector
BoxOptimProblem. lowerBound()
Gets the lower bounds.Vector
BoxOptimProblem. upperBound()
Gets the upper bounds.Constructors in dev.nm.solver.multivariate.constrained.problem with parameters of type Vector Constructor Description BoxOptimProblem(RealScalarFunction f, Vector lower, Vector upper)
Constructs an optimization problem with box constraints. -
Uses of Vector in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
DEOptim. solve(OptimProblem problem)
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim with parameters of type Vector Modifier and Type Method Description Best2Bin.DeBest2BinCell
Best2Bin. getSimpleCell(RealScalarFunction f, Vector x)
Rand1Bin.DeRand1BinCell
Rand1Bin. getSimpleCell(RealScalarFunction f, Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim with parameters of type Vector Constructor Description DeBest2BinCell(RealScalarFunction f, Vector x)
DeOptimCell(RealScalarFunction f, Vector x)
DeRand1BinCell(RealScalarFunction f, Vector x)
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Uses of Vector in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained that return Vector Modifier and Type Method Description Vector
IntegralConstrainedCellFactory.AllIntegers. round(Vector x)
Vector
IntegralConstrainedCellFactory.IntegerConstraint. round(Vector x)
Vector
IntegralConstrainedCellFactory.SomeIntegers. round(Vector x)
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained with parameters of type Vector Modifier and Type Method Description abstract ConstrainedCellFactory.ConstrainedCell
ConstrainedCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Override this method to put in whatever constraints in the minimization problem.ConstrainedCellFactory.ConstrainedCell
IntegralConstrainedCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Vector
IntegralConstrainedCellFactory.AllIntegers. round(Vector x)
Vector
IntegralConstrainedCellFactory.IntegerConstraint. round(Vector x)
Vector
IntegralConstrainedCellFactory.SomeIntegers. round(Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.deoptim.constrained with parameters of type Vector Constructor Description ConstrainedCell(RealScalarFunction f, Vector x)
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Uses of Vector in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local with parameters of type Vector Modifier and Type Method Description LocalSearchCellFactory.LocalSearchCell
LocalSearchCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.local with parameters of type Vector Constructor Description LocalSearchCell(RealScalarFunction f, Vector x)
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Uses of Vector in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid
Fields in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid declared as Vector Modifier and Type Field Description protected Vector[]
SimpleGridMinimizer.Solution. initials
protected Vector
SimpleGridMinimizer.Solution. xmin
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid that return Vector Modifier and Type Method Description Vector
SimpleGridMinimizer.Solution. search(Vector... initials)
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
SimpleGridMinimizer. solve(OptimProblem problem)
Methods in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid with parameters of type Vector Modifier and Type Method Description SimpleCellFactory.SimpleCell
SimpleCellFactory. getSimpleCell(RealScalarFunction f, Vector x)
Construct an instance of aSimpleCell
.Vector
SimpleGridMinimizer.Solution. search(Vector... initials)
void
SimpleGridMinimizer.Solution. setInitials(Vector... initials)
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid with parameters of type Vector Constructor Description RealScalarFunctionChromosome(RealScalarFunction f, Vector x)
Construct an instance ofRealScalarFunctionChromosome
.SimpleCell(RealScalarFunction f, Vector x)
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Uses of Vector in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration
Constructors in dev.nm.solver.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration with parameters of type Vector Constructor Description PerturbationAroundPoint(RealScalarFunction f, SimpleCellFactory factory, int poolSize, Vector var, Vector initial0, long seed)
Generate an initial pool of chromosomes by adding a variance around a given initial.UniformMeshOverRegion(RealScalarFunction f, SimpleCellFactory factory, RandomLongGenerator uniform, int minDiscretization, Vector[] initials0, double epsilon)
Generate an initial pool of chromosomes by putting a uniform mesh/grid/net over the entire region. -
Uses of Vector in dev.nm.solver.multivariate.initialization
Methods in dev.nm.solver.multivariate.initialization that return Vector Modifier and Type Method Description Vector[]
DefaultSimplex. getInitials(Vector... initials)
Build a simplex of N+1 vertices from an initial point, where N is the dimension of the initial points.Vector[]
InitialsFactory. getInitials(Vector... initials)
Generate a set of initial points for optimization from the fewer than required points.Vector[]
UniformDistributionOverBox1. getInitials()
Generate a set of initial points for optimization.Vector[]
UniformDistributionOverBox1. getInitials(Vector... notused)
Vector[]
UniformDistributionOverBox2. getInitials()
Generate a set of initial points for optimization.Vector[]
UniformDistributionOverBox2. getInitials(Vector... notused)
Methods in dev.nm.solver.multivariate.initialization with parameters of type Vector Modifier and Type Method Description Vector[]
DefaultSimplex. getInitials(Vector... initials)
Build a simplex of N+1 vertices from an initial point, where N is the dimension of the initial points.Vector[]
InitialsFactory. getInitials(Vector... initials)
Generate a set of initial points for optimization from the fewer than required points.Vector[]
UniformDistributionOverBox1. getInitials(Vector... notused)
Vector[]
UniformDistributionOverBox2. getInitials(Vector... notused)
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Uses of Vector in dev.nm.solver.multivariate.minmax
Methods in dev.nm.solver.multivariate.minmax that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
LeastPth. solve(MinMaxProblem<T> problem)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained
Classes in dev.nm.solver.multivariate.unconstrained with type parameters of type Vector Modifier and Type Interface Description interface
MultivariateMinimizer<P extends OptimProblem,S extends MinimizationSolution<Vector>>
This is a minimizer that minimizes a multivariate function or a Vector function.Methods in dev.nm.solver.multivariate.unconstrained that return Vector Modifier and Type Method Description Vector
BruteForceMinimizer.Solution. minimizer()
Method parameters in dev.nm.solver.multivariate.unconstrained with type arguments of type Vector Modifier and Type Method Description void
BruteForceMinimizer.Solution. setDomain(List<Vector> domain)
Gives the domain values for the brute force search to try.BruteForceMinimizer.Solution
BruteForceMinimizer. solve(Function<Vector,R> f)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained.annealing
Methods in dev.nm.solver.multivariate.unconstrained.annealing that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
SimulatedAnnealingMinimizer. solve(OptimProblem problem)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction
Methods in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction with parameters of type Vector Modifier and Type Method Description double
BoxGSAAcceptanceProbabilityFunction. acceptanceProbability(Vector currentState, double currentEnergy, Vector proposedState, double proposedEnergy, double temperature)
double
GSAAcceptanceProbabilityFunction. acceptanceProbability(Vector currentState, double currentEnergy, Vector proposedState, double proposedEnergy, double temperature)
double
MetropolisAcceptanceProbabilityFunction. acceptanceProbability(Vector currentState, double currentEnergy, Vector proposedState, double proposedEnergy, double temperature)
double
TemperedAcceptanceProbabilityFunction. acceptanceProbability(Vector currentState, double energyCurrent, Vector proposedState, double energyProposed, double temperature)
Computes the probability that the next state transition will be accepted.Constructors in dev.nm.solver.multivariate.unconstrained.annealing.acceptanceprobabilityfunction with parameters of type Vector Constructor Description BoxGSAAcceptanceProbabilityFunction(Vector lower, Vector upper, double qa)
Constructs a boxed acceptance probability function. -
Uses of Vector in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction
Methods in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction that return Vector Modifier and Type Method Description Vector
AnnealingFunction. nextProposal(Vector currentState, double temperature)
Gets the next proposal, given the current state and the temperature.Vector
BoxGSAAnnealingFunction. nextProposal(Vector currentState, double temperature)
Vector
GSAAnnealingFunction. nextProposal(Vector currentState, double temperature)
Vector
SimpleAnnealingFunction. nextProposal(Vector x0, double temperature)
Methods in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction with parameters of type Vector Modifier and Type Method Description Vector
AnnealingFunction. nextProposal(Vector currentState, double temperature)
Gets the next proposal, given the current state and the temperature.Vector
BoxGSAAnnealingFunction. nextProposal(Vector currentState, double temperature)
Vector
GSAAnnealingFunction. nextProposal(Vector currentState, double temperature)
Vector
SimpleAnnealingFunction. nextProposal(Vector x0, double temperature)
Constructors in dev.nm.solver.multivariate.unconstrained.annealing.annealingfunction with parameters of type Vector Constructor Description BoxGSAAnnealingFunction(Vector lower, Vector upper, double qv, RandomLongGenerator uniform)
Constructs a boxed annealing function. -
Uses of Vector in dev.nm.solver.multivariate.unconstrained.c2
Methods in dev.nm.solver.multivariate.unconstrained.c2 that return Vector Modifier and Type Method Description Vector
NelderMeadMinimizer.Solution. search(Vector... simplex)
Perform a Nelder-Mead search from an initial simplex.Methods in dev.nm.solver.multivariate.unconstrained.c2 with parameters of type Vector Modifier and Type Method Description Vector
NelderMeadMinimizer.Solution. search(Vector... simplex)
Perform a Nelder-Mead search from an initial simplex.void
NelderMeadMinimizer.Solution. setInitials(Vector... simplex)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection
Methods in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection that return Vector Modifier and Type Method Description Vector
PowellMinimizer.PowellImpl. getDirection(Vector xk)
Vector
ZangwillMinimizer.ZangwillImpl. getDirection(Vector xk)
Methods in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
ConjugateGradientMinimizer. solve(C2OptimProblem problem)
IterativeSolution<Vector>
FletcherReevesMinimizer. solve(C2OptimProblem problem)
IterativeSolution<Vector>
PowellMinimizer. solve(C2OptimProblem problem)
IterativeSolution<Vector>
ZangwillMinimizer. solve(C2OptimProblem problem)
Methods in dev.nm.solver.multivariate.unconstrained.c2.conjugatedirection with parameters of type Vector Modifier and Type Method Description Vector
PowellMinimizer.PowellImpl. getDirection(Vector xk)
Vector
ZangwillMinimizer.ZangwillImpl. getDirection(Vector xk)
double
PowellMinimizer.PowellImpl. getIncrement(Vector xk, Vector dk)
double
ZangwillMinimizer.ZangwillImpl. getIncrement(Vector xk, Vector dk)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained.c2.linesearch
Methods in dev.nm.solver.multivariate.unconstrained.c2.linesearch with parameters of type Vector Modifier and Type Method Description double
LineSearch.Solution. linesearch(Vector x, Vector d)
Get the increment α so that f(x + α * d) is (approximately) minimized. -
Uses of Vector in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
Fields in dev.nm.solver.multivariate.unconstrained.c2.quasinewton declared as Vector Modifier and Type Field Description protected Vector
QuasiNewtonMinimizer.QuasiNewtonImpl. dk
the line search direction at the k-th iterationprotected Vector
QuasiNewtonMinimizer.QuasiNewtonImpl. gk
the gradient at the k-th iterationMethods in dev.nm.solver.multivariate.unconstrained.c2.quasinewton that return Vector Modifier and Type Method Description Vector
QuasiNewtonMinimizer.QuasiNewtonImpl. getDirection(Vector xk)
Methods in dev.nm.solver.multivariate.unconstrained.c2.quasinewton that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
BFGSMinimizer. solve(C2OptimProblem problem)
IterativeSolution<Vector>
HuangMinimizer. solve(C2OptimProblem problem)
Methods in dev.nm.solver.multivariate.unconstrained.c2.quasinewton with parameters of type Vector Modifier and Type Method Description static Matrix
BFGSMinimizer. dampedBFGSHessianUpdate(Matrix H, Vector gamma, Vector delta)
Damped BFGS Hessian update.Vector
QuasiNewtonMinimizer.QuasiNewtonImpl. getDirection(Vector xk)
double
QuasiNewtonMinimizer.QuasiNewtonImpl. getIncrement(Vector xk, Vector dk)
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Uses of Vector in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent
Methods in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent that return Vector Modifier and Type Method Description Vector
GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl. getDirection(Vector xk)
Vector
NewtonRaphsonMinimizer.NewtonRaphsonImpl. getDirection(Vector xk)
protected abstract Vector
SteepestDescentMinimizer.SteepestDescentImpl. getDirection(Vector xk)
Get the next search direction.Vector
SteepestDescentMinimizer.SteepestDescentImpl. search(Vector... initials)
Vector
SteepestDescentMinimizer.SteepestDescentImpl. step()
Methods in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent that return types with arguments of type Vector Modifier and Type Method Description IterativeSolution<Vector>
FirstOrderMinimizer. solve(C2OptimProblem problem)
IterativeSolution<Vector>
GaussNewtonMinimizer.MySteepestDescent. solve(C2OptimProblem problem)
IterativeSolution<Vector>
GaussNewtonMinimizer. solve(RealVectorFunction vf)
Solve the minimization problem to minimize F = vf' * vf.IterativeSolution<Vector>
GaussNewtonMinimizer. solve(RealVectorFunction vf, RntoMatrix J)
Solve the minimization problem to minimize F = vf' * vf.IterativeSolution<Vector>
NewtonRaphsonMinimizer. solve(C2OptimProblem problem)
abstract IterativeSolution<Vector>
SteepestDescentMinimizer. solve(C2OptimProblem problem)
Solve a minimization problem with a C2 objective function.Methods in dev.nm.solver.multivariate.unconstrained.c2.steepestdescent with parameters of type Vector Modifier and Type Method Description Vector
GaussNewtonMinimizer.MySteepestDescent.GaussNewtonImpl. getDirection(Vector xk)
Vector
NewtonRaphsonMinimizer.NewtonRaphsonImpl. getDirection(Vector xk)
protected abstract Vector
SteepestDescentMinimizer.SteepestDescentImpl. getDirection(Vector xk)
Get the next search direction.protected double
SteepestDescentMinimizer.SteepestDescentImpl. getIncrement(Vector xk, Vector dk)
Get the increment fraction, αk.Vector
SteepestDescentMinimizer.SteepestDescentImpl. search(Vector... initials)
void
SteepestDescentMinimizer.SteepestDescentImpl. setInitials(Vector... initials)
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Uses of Vector in dev.nm.stat.cointegration
Methods in dev.nm.stat.cointegration that return Vector Modifier and Type Method Description Vector
CointegrationMLE. beta(int r)
Get the r-th cointegrating factor, counting from 1.Vector
CointegrationMLE. getEigenvalues()
Get the set of real eigenvalues.Vector
JohansenTest. getStats(CointegrationMLE coint)
Get the set of likelihood ratio test statistics for testing H(r) in H(r+1). -
Uses of Vector in dev.nm.stat.covariance.nlshrink
Methods in dev.nm.stat.covariance.nlshrink that return Vector Modifier and Type Method Description Vector
TauEstimator. estimatedPopulationEigenvalues(Vector initials)
Estimates population eigenvalues from given sample eigenvalues.Vector
LedoitWolf2016.Result. lambda()
Gets sample eigenvalues.Vector
LedoitWolf2016.Result. linshrink_tau()
Gets linear shrinkage tau in ascending order.Vector
LedoitWolf2016.Result. nlshrink_tau()
Gets nonlinear shrinkage tau in ascending order.Vector
NonlinearShrinkageEstimator. nonlinearShrunkEigenvalues()
Gets the nonlinear shrinkage eigenvalues in ascending order.Vector
LedoitWolf2016.Result. tau()
Gets estimated population eigenvalues in ascending order.Methods in dev.nm.stat.covariance.nlshrink with parameters of type Vector Modifier and Type Method Description Vector
TauEstimator. estimatedPopulationEigenvalues(Vector initials)
Estimates population eigenvalues from given sample eigenvalues.Constructors in dev.nm.stat.covariance.nlshrink with parameters of type Vector Constructor Description NonlinearShrinkageEstimator(Vector tau, int n)
TauEstimator(int p, int n, Vector sample_lambdas)
TauEstimator(int p, int n, Vector sample_lambdas, int max_optimizer_iterations, double epsilon)
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Uses of Vector in dev.nm.stat.covariance.nlshrink.quest
Fields in dev.nm.stat.covariance.nlshrink.quest declared as Vector Modifier and Type Field Description Vector
QuEST.Result. estimated_lambda
estimated lambdaVector
QuEST.Result. pw
number of each distinct eigenvaluesVector
QuEST.Result. t
distinct population eigenvaluesVector
QuEST.Result. tau
population eigenvalues in ascending orderConstructors in dev.nm.stat.covariance.nlshrink.quest with parameters of type Vector Constructor Description QuEST(Vector tau, int n)
QuEST(Vector tau, int n, double tol, double left_tol)
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Uses of Vector in dev.nm.stat.descriptive.correlation
Methods in dev.nm.stat.descriptive.correlation that return Vector Modifier and Type Method Description Vector
CorrelationMatrix. stdev()
Gets the standard deviations of the elements.Vector
CorrelationMatrix. var()
Gets the variance of the elements. -
Uses of Vector in dev.nm.stat.distribution.multivariate
Methods in dev.nm.stat.distribution.multivariate that return Vector Modifier and Type Method Description Vector
DirichletDistribution. mean()
Vector
MultinomialDistribution. mean()
Vector
MultivariateNormalDistribution. mean()
Vector
MultivariateProbabilityDistribution. mean()
Gets the mean of this distribution.Vector
MultivariateTDistribution. mean()
Vector
DirichletDistribution. mode()
Vector
MultinomialDistribution. mode()
Vector
MultivariateNormalDistribution. mode()
Vector
MultivariateProbabilityDistribution. mode()
Gets the mode of this distribution.Vector
MultivariateTDistribution. mode()
Methods in dev.nm.stat.distribution.multivariate with parameters of type Vector Modifier and Type Method Description double
AbstractBivariateProbabilityDistribution. cdf(Vector x)
double
DirichletDistribution. cdf(Vector x)
double
MultinomialDistribution. cdf(Vector x)
double
MultivariateNormalDistribution. cdf(Vector x)
double
MultivariateProbabilityDistribution. cdf(Vector x)
Gets the cumulative probability F(x) = Pr(X ≤ x).double
MultivariateTDistribution. cdf(Vector x)
double
AbstractBivariateProbabilityDistribution. density(Vector x)
double
DirichletDistribution. density(Vector x)
double
MultinomialDistribution. density(Vector x)
double
MultivariateNormalDistribution. density(Vector x)
double
MultivariateProbabilityDistribution. density(Vector x)
The density function, which, if exists, is the derivative of F.double
MultivariateTDistribution. density(Vector x)
double
DirichletDistribution. moment(Vector t)
double
MultinomialDistribution. moment(Vector t)
double
MultivariateNormalDistribution. moment(Vector t)
double
MultivariateProbabilityDistribution. moment(Vector t)
The moment generating function is the expected value of etX.double
MultivariateTDistribution. moment(Vector t)
Constructors in dev.nm.stat.distribution.multivariate with parameters of type Vector Constructor Description MultivariateNormalDistribution(Vector mu, Matrix Sigma)
Constructs an instance with the given mean and covariance matrix.MultivariateTDistribution(int v, Vector mu, Matrix Sigma)
Constructs an instance with the given mean and scale matrix. -
Uses of Vector in dev.nm.stat.distribution.multivariate.exponentialfamily
Methods in dev.nm.stat.distribution.multivariate.exponentialfamily with parameters of type Vector Modifier and Type Method Description MultivariateProbabilityDistribution
MultivariateExponentialFamily. getDistribution(Vector theta)
Construct a probability distribution in the exponential family. -
Uses of Vector in dev.nm.stat.distribution.univariate.exponentialfamily
Methods in dev.nm.stat.distribution.univariate.exponentialfamily with parameters of type Vector Modifier and Type Method Description ProbabilityDistribution
ExponentialFamily. getDistribution(Vector theta)
Construct a probability distribution in the exponential family. -
Uses of Vector in dev.nm.stat.dlm.multivariate
Methods in dev.nm.stat.dlm.multivariate that return Vector Modifier and Type Method Description Vector
MultivariateStateEquation. xt_mean(int t, Vector xt_1)
Predicts the next state without control variable.Vector
MultivariateStateEquation. xt_mean(int t, Vector xt_1, Vector ut)
Predicts the next state.Methods in dev.nm.stat.dlm.multivariate with parameters of type Vector Modifier and Type Method Description MultivariateDLMSim.Innovation
MultivariateDLMSim. next(Vector u)
Gets the next innovation.ImmutableVector
MultivariateStateEquation. xt(int t, Vector xt_1)
Evaluates the state equation without the control variable.ImmutableVector
MultivariateStateEquation. xt(int t, Vector xt_1, Vector ut)
Evaluates the state equation.Vector
MultivariateStateEquation. xt_mean(int t, Vector xt_1)
Predicts the next state without control variable.Vector
MultivariateStateEquation. xt_mean(int t, Vector xt_1, Vector ut)
Predicts the next state.ImmutableVector
MultivariateObservationEquation. yt(int t, Vector xt)
Evaluates the observation equation.ImmutableVector
MultivariateObservationEquation. yt_mean(int t, Vector xt)
Predicts the next observation.Constructors in dev.nm.stat.dlm.multivariate with parameters of type Vector Constructor Description MultivariateDLM(Vector m0, Matrix C0, MultivariateObservationEquation Yt, MultivariateStateEquation Xt)
Construct a (multivariate) controlled dynamic linear model. -
Uses of Vector in dev.nm.stat.evt.evd.bivariate
Methods in dev.nm.stat.evt.evd.bivariate that return Vector Modifier and Type Method Description Vector
AbstractBivariateEVD. mean()
Vector
AbstractBivariateEVD. mode()
Methods in dev.nm.stat.evt.evd.bivariate with parameters of type Vector Modifier and Type Method Description double
AbstractBivariateEVD. moment(Vector t)
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Uses of Vector in dev.nm.stat.evt.evd.univariate.fitting
Methods in dev.nm.stat.evt.evd.univariate.fitting that return Vector Modifier and Type Method Description Vector
ConfidenceInterval. getLower()
Get the lower bounds of the confidence intervals.Vector
ConfidenceInterval. getMean()
Get the mean values.Vector
ConfidenceInterval. getUpper()
Get the upper bounds of the confidence intervals.Vector
EstimateByLogLikelihood. standardError()
Get the standard errors of the fitted parameters.Constructors in dev.nm.stat.evt.evd.univariate.fitting with parameters of type Vector Constructor Description ConfidenceInterval(double confidenceLevel, Vector mean, Vector lower, Vector upper)
Create an instance with the confidence interval information.EstimateByLogLikelihood(Vector fittedParameters, RealScalarFunction logLikelihoodFunction)
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Uses of Vector in dev.nm.stat.factor.factoranalysis
Methods in dev.nm.stat.factor.factoranalysis with parameters of type Vector Modifier and Type Method Description FAEstimator
FactorAnalysis. getEstimators(Vector initial, int maxIterations)
Gets the estimators (estimated psi, loading matrix, degree of freedom, test statistics, p-value, etc) obtained from the factor analysis, given the initial psi and the maximum number of iterations. -
Uses of Vector in dev.nm.stat.factor.implicitmodelpca
Methods in dev.nm.stat.factor.implicitmodelpca that return Vector Modifier and Type Method Description Vector
ExplicitImplicitModelPCA.Result. b(int n)
Gets the implicit factor loading for the n-th subject.Vector
ImplicitModelPCA.Result. b(int n)
Gets the factor loading for the n-th subject.Vector
ExplicitImplicitModelPCA.Result. e_t(int t)
Gets the residual of all subject at time t.Vector
ImplicitModelPCA.Result. e_t(int t)
Gets the residual of all subject at time t.Vector
ExplicitImplicitModelPCA.Result. f(int t)
Gets the implicit factor values at time t.Vector
ImplicitModelPCA.Result. f(int t)
Gets the factor values at time t.Vector
ExplicitImplicitModelPCA.Result. R_bar()
Gets R_bar, the average of observations over time per subject.Vector
ImplicitModelPCA.Result. R_bar()
Gets R_bar, the average of observations over time per subject. -
Uses of Vector in dev.nm.stat.factor.pca
Methods in dev.nm.stat.factor.pca that return Vector Modifier and Type Method Description Vector
PCA. cumulativeProportionVar()
Gets the cumulative proportion of overall variance explained by the principal componentsVector
PCA. loading(int i)
Gets the loading vector of the i-th principal component.Vector
PCAbyEigen. loading(int i)
Vector
PCA. mean()
Gets the sample means that were subtracted.Vector
PCAbySVD. mean()
Vector
PCA. proportionVar()
Gets the proportion of overall variance explained by each of the principal components.Vector
PCAbyEigen. proportionVar()
Vector
PCA. scale()
Gets the scalings applied to each variable.Vector
PCAbyEigen. scale()
Gets the scalings applied to each variable.Vector
PCAbySVD. scale()
Vector
PCA. sdPrincipalComponents()
Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).Vector
PCAbyEigen. sdPrincipalComponents()
Gets the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the correlation (or covariance) matrix).Constructors in dev.nm.stat.factor.pca with parameters of type Vector Constructor Description PCAbySVD(Matrix data, Vector mean, Vector scale)
Performs Principal Component Analysis, using the preferred SVD method, on a data matrix with (optional) mean vector and scaling vector provided. -
Uses of Vector in dev.nm.stat.hmm
Methods in dev.nm.stat.hmm that return Vector Modifier and Type Method Description Vector
ForwardBackwardProcedure. scaledAlpha(int t)
Gets the scaled forward probabilities at time t.Vector
ForwardBackwardProcedure. scaledBeta(int t)
Gets the scaled backward probabilities at time t.Constructors in dev.nm.stat.hmm with parameters of type Vector Constructor Description HiddenMarkovModel(Vector PI, Matrix A, RandomNumberGenerator[] B)
HMMRNG(Vector PI, Matrix A, RandomNumberGenerator[] B)
Constructs a hidden Markov model. -
Uses of Vector in dev.nm.stat.hmm.discrete
Methods in dev.nm.stat.hmm.discrete that return Vector Modifier and Type Method Description static Vector[]
BaumWelch. gamma(HiddenMarkovModel model, int[] observations, Matrix[] xi)
Gets the (T-1 * N) γ matrix, where the (t, i)-th entry is γt(i).Constructors in dev.nm.stat.hmm.discrete with parameters of type Vector Constructor Description DiscreteHMM(Vector PI, Matrix A, Matrix B)
Constructs a discrete hidden Markov model. -
Uses of Vector in dev.nm.stat.hmm.mixture
Constructors in dev.nm.stat.hmm.mixture with parameters of type Vector Constructor Description MixtureHMM(Vector PI, Matrix A, MixtureDistribution dist)
Constructs a mixture hidden Markov model. -
Uses of Vector in dev.nm.stat.hmm.mixture.distribution
Methods in dev.nm.stat.hmm.mixture.distribution with parameters of type Vector Modifier and Type Method Description BetaMixtureDistribution.Lambda[]
BetaMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
BinomialMixtureDistribution.Lambda[]
BinomialMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
Double[]
ExponentialMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
GammaMixtureDistribution.Lambda[]
GammaMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
LogNormalMixtureDistribution.Lambda[]
LogNormalMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
Object[]
MixtureDistribution. getMStepParams(double[] observations, Vector[] u)
Maximize, for each state, the log-likelihood of the distribution with respect to the observations and current estimators.NormalMixtureDistribution.Lambda[]
NormalMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
Double[]
PoissonMixtureDistribution. getMStepParams(double[] observations, Vector[] u)
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Uses of Vector in dev.nm.stat.markovchain
Methods in dev.nm.stat.markovchain that return Vector Modifier and Type Method Description static Vector
MCUtils. getStateCounts(int[] states)
Count the numbers of occurrences of states.static Vector
SimpleMC. getStationaryProbabilities(Matrix A)
Gets the stationary state probabilities of a Markov chain that is irreducible, aperiodic and strongly connected (positive recurrent).Constructors in dev.nm.stat.markovchain with parameters of type Vector Constructor Description SimpleMC(Vector PI, Matrix A)
Constructs a time-homogeneous Markov chain with a finite state space. -
Uses of Vector in dev.nm.stat.random.rng.multivariate
Constructors in dev.nm.stat.random.rng.multivariate with parameters of type Vector Constructor Description NormalRVG(Vector mu, Matrix sigma)
Constructs a multivariate Normal random vector generator.NormalRVG(Vector mu, Matrix sigma, double epsilon, RandomStandardNormalGenerator rnorm)
Constructs a multivariate Normal random vector generator.NormalRVG(Vector mu, Matrix sigma, RandomStandardNormalGenerator rnorm)
Constructs a multivariate Normal random vector generator. -
Uses of Vector in dev.nm.stat.random.rng.multivariate.mcmc.hybrid
Methods in dev.nm.stat.random.rng.multivariate.mcmc.hybrid that return Vector Modifier and Type Method Description protected Vector
HybridMCMC. nextProposedState(Vector currentState)
protected Vector
MultipointHybridMCMC. nextProposedState(Vector currentState)
Vector
LeapFrogging.DynamicsState. p()
Gets the momentum.Vector
LeapFrogging. p()
Gets the current momentum.Vector
LeapFrogging.DynamicsState. x()
Gets the position.Vector
LeapFrogging. x()
Gets the current position.Methods in dev.nm.stat.random.rng.multivariate.mcmc.hybrid with parameters of type Vector Modifier and Type Method Description static double
AbstractHybridMCMC. H(LeapFrogging.DynamicsState state, RealScalarFunction logF, Vector m)
Evaluates a system's total energy at a given state.protected boolean
HybridMCMC. isProposalAccepted(Vector currentState, Vector proposedState)
protected boolean
MultipointHybridMCMC. isProposalAccepted(Vector currentState, Vector proposedState)
static double
AbstractHybridMCMC. k(Vector p, Vector m)
Evaluates the standard kinetic energy, k = p^2 / 2m.protected Vector
HybridMCMC. nextProposedState(Vector currentState)
protected Vector
MultipointHybridMCMC. nextProposedState(Vector currentState)
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.hybrid with parameters of type Vector Constructor Description AbstractHybridMCMC(Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters.DynamicsState(Vector x, Vector p)
Constructs a new instance with the given position and momentum.HybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, Vector initialState, RandomLongGenerator rlg)
Constructs a new instance with the given parameters.LeapFrogging(RealVectorFunction dU, Vector m, Vector x, Vector p, double dt)
Constructs a new instance with the given parameters.MultipointHybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, int M, Vector w, Vector initialState, RandomLongGenerator uniform)
Constructs a new instance with the given parameters.MultipointHybridMCMC(RealScalarFunction logF, RealVectorFunction dLogF, Vector m, double dt, int L, int M, Vector initialState, RandomLongGenerator uniform)
Constructs a new instance with equal weights to the M configurations. -
Uses of Vector in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Methods in dev.nm.stat.random.rng.multivariate.mcmc.metropolis that return Vector Modifier and Type Method Description protected abstract Vector
AbstractMetropolis. nextProposedState(Vector currentState)
Proposes a next state for the system.protected Vector
Metropolis. nextProposedState(Vector currentState)
protected Vector
MetropolisHastings. nextProposedState(Vector currentState)
protected Vector
RobustAdaptiveMetropolis. nextProposedState(Vector currentState)
Methods in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type Vector Modifier and Type Method Description double
MetropolisHastings.ProposalDensityFunction. evaluate(Vector x, Vector y)
Evaluates the density at the given points.protected abstract boolean
AbstractMetropolis. isProposalAccepted(Vector currentState, Vector proposedState)
Decides whether the given proposed state should be accepted, or whether the system should remain in it's current state.protected boolean
Metropolis. isProposalAccepted(Vector currentState, Vector proposedState)
protected boolean
MetropolisHastings. isProposalAccepted(Vector x, Vector y)
static boolean
MetropolisUtils. isProposalAccepted(RealScalarFunction logf, RandomLongGenerator uniform, Vector currentState, Vector proposedState)
Uses the given LOG density function to determine whether the given state transition should be accepted.protected boolean
RobustAdaptiveMetropolis. isProposalAccepted(Vector currentState, Vector proposedState)
static double
MetropolisUtils. logAcceptanceRatio(RealScalarFunction logf, Vector currentState, Vector proposedState)
Computes the log of the acceptance ratio.protected abstract Vector
AbstractMetropolis. nextProposedState(Vector currentState)
Proposes a next state for the system.protected Vector
Metropolis. nextProposedState(Vector currentState)
protected Vector
MetropolisHastings. nextProposedState(Vector currentState)
protected Vector
RobustAdaptiveMetropolis. nextProposedState(Vector currentState)
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type Vector Constructor Description AbstractMetropolis(Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters.Metropolis(RealScalarFunction logf, Vector initialState, double sigma, RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, with the given variance and zero covariance.Metropolis(RealScalarFunction logf, Vector initialState, Matrix scale, RandomLongGenerator uniform)
Constructs a new instance, which draws the offset of the next proposed state from the previous state from a standard Normal distribution, multiplied by the given scale matrix.Metropolis(RealScalarFunction logf, RealVectorFunction proposalFunction, Vector initialState, RandomLongGenerator uniform)
Constructs a new instance with the given parameters.MetropolisHastings(RealScalarFunction logf, ProposalFunction proposalFunction, MetropolisHastings.ProposalDensityFunction proposalDensity, Vector initialState, RandomNumberGenerator rng)
Constructs a new instance with the given parameters.RobustAdaptiveMetropolis(RealScalarFunction logf, double targetAcceptance, Vector initialState, RandomLongGenerator uniform)
Constructs an instance which assumes an initial variance of 1 per variable, uses a gamma of 0.5.RobustAdaptiveMetropolis(RealScalarFunction logf, Matrix initialScale, double gamma, double targetAcceptance, Vector initialState, RandomStandardNormalGenerator rnorm, RandomLongGenerator uniform)
Constructs a new instance with the given parameters. -
Uses of Vector in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
Methods in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction that return Vector Modifier and Type Method Description Vector
GaussianProposalFunction. evaluate(Vector x)
Vector
HybridMCMCProposalFunction. evaluate(Vector x)
Methods in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction with parameters of type Vector Modifier and Type Method Description Vector
GaussianProposalFunction. evaluate(Vector x)
Vector
HybridMCMCProposalFunction. evaluate(Vector x)
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction with parameters of type Vector Constructor Description HybridMCMCProposalFunction(Vector m, RandomLongGenerator uniform)
Constructs a hybrid MC proposal function. -
Uses of Vector in dev.nm.stat.regression.linear
Methods in dev.nm.stat.regression.linear with parameters of type Vector Modifier and Type Method Description double
LinearModel. Ey(Vector x)
Computes the expectation \(E(y(x))\) given an input.Constructors in dev.nm.stat.regression.linear with parameters of type Vector Constructor Description LMBeta(Vector betaHat)
Constructs an instance ofBeta
.LMProblem(Vector y, Matrix X)
Constructs a linear regression problem, assuming a constant term (the intercept) equal weights assigned to all observationsLMProblem(Vector y, Matrix X, boolean intercept)
Constructs a linear regression problem, assuming equal weights to all observations.LMProblem(Vector y, Matrix X, boolean intercept, Vector weights)
Constructs a linear regression problem.LMProblem(Vector y, Matrix X, Vector weights)
Constructs a linear regression problem, assuming a constant term (the intercept). -
Uses of Vector in dev.nm.stat.regression.linear.glm
Methods in dev.nm.stat.regression.linear.glm with parameters of type Vector Modifier and Type Method Description double
GeneralizedLinearModel. Ey(Vector x)
void
GLMFitting. fit(GLMProblem problem, Vector beta0Initial)
Fits a Generalized Linear Model.void
IWLS. fit(GLMProblem probelm, Vector beta0Initial)
Constructors in dev.nm.stat.regression.linear.glm with parameters of type Vector Constructor Description GLMProblem(Vector y, Matrix X, boolean intercept, GLMFamily family)
Construct a GLM problem.GLMResiduals(GLMProblem problem, Vector fitted)
Performs residual analysis for a GLM regression. -
Uses of Vector in dev.nm.stat.regression.linear.glm.distribution
Methods in dev.nm.stat.regression.linear.glm.distribution with parameters of type Vector Modifier and Type Method Description double
GLMBinomial. AIC(Vector y, Vector mu, Vector weight, double prelogLike, double deviance, int nFactors)
double
GLMExponentialDistribution. AIC(Vector y, Vector mu, Vector weight, double preLogLike, double deviance, int nFactors)
AIC = 2 * #param - 2 * log-likelihooddouble
GLMGamma. AIC(Vector y, Vector mu, Vector weight, double prelogLike, double deviance, int nFactors)
double
GLMGaussian. AIC(Vector y, Vector mu, Vector weight, double preLogLike, double deviance, int nFactors)
double
GLMInverseGaussian. AIC(Vector y, Vector mu, Vector weight, double prelogLike, double deviance, int nFactors)
double
GLMPoisson. AIC(Vector y, Vector mu, Vector weight, double preLogLike, double deviance, int nFactors)
double
GLMBinomial. dispersion(Vector y, Vector mu, int nFactors)
double
GLMExponentialDistribution. dispersion(Vector y, Vector mu, int nFactors)
Different distribution models have different ways to compute dispersion, Φ.double
GLMGamma. dispersion(Vector y, Vector mu, int nFactors)
double
GLMGaussian. dispersion(Vector y, Vector mu, int nFactors)
double
GLMInverseGaussian. dispersion(Vector y, Vector mu, int nFactors)
double
GLMPoisson. dispersion(Vector y, Vector mu, int nFactors)
double
GLMBinomial. overdispersion(Vector y, Vector mu, int nFactors)
double
GLMExponentialDistribution. overdispersion(Vector y, Vector mu, int nFactors)
Over-dispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on the nominal variance of a given simple statistical model.double
GLMGamma. overdispersion(Vector y, Vector mu, int nFactors)
double
GLMGaussian. overdispersion(Vector y, Vector mu, int nFactors)
double
GLMInverseGaussian. overdispersion(Vector y, Vector mu, int nFactors)
double
GLMPoisson. overdispersion(Vector y, Vector mu, int nFactors)
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Uses of Vector in dev.nm.stat.regression.linear.glm.quasi
Methods in dev.nm.stat.regression.linear.glm.quasi with parameters of type Vector Modifier and Type Method Description double
GeneralizedLinearModelQuasiFamily. Ey(Vector x)
void
QuasiGLMNewtonRaphson. fit(GLMProblem problem, Vector beta0Initial)
Constructors in dev.nm.stat.regression.linear.glm.quasi with parameters of type Vector Constructor Description QuasiGLMProblem(Vector y, Matrix X, boolean intercept, QuasiFamily quasiFamily)
Constructs a quasi GLM problem. -
Uses of Vector in dev.nm.stat.regression.linear.lasso
Methods in dev.nm.stat.regression.linear.lasso with parameters of type Vector Modifier and Type Method Description double
ConstrainedLASSObyLARS. Ey(Vector x)
double
ConstrainedLASSObyQP. Ey(Vector x)
double
UnconstrainedLASSObyCoordinateDescent. Ey(Vector x)
double
UnconstrainedLASSObyQP. Ey(Vector x)
Constructors in dev.nm.stat.regression.linear.lasso with parameters of type Vector Constructor Description ConstrainedLASSOProblem(Vector y, Matrix X, double t)
Constructs a LASSO problem in the constrained form.UnconstrainedLASSOProblem(Vector y, Matrix X, double lambda)
Constructs a LASSO problem. -
Uses of Vector in dev.nm.stat.regression.linear.lasso.lars
Methods in dev.nm.stat.regression.linear.lasso.lars that return Vector Modifier and Type Method Description Vector
LARSProblem. XL2Norm()
Gets the L2 norms of the covariates (a vector of ones if no standardization is required).Vector
LARSProblem. XMean()
Gets the mean vector to be subtracted from the covariates (a vector of zeros if no intercept is included).Vector
LARSProblem. yLARS()
Gets the vector of response variable (possibly demeaned) to be used in LARS.Methods in dev.nm.stat.regression.linear.lasso.lars that return types with arguments of type Vector Modifier and Type Method Description List<Vector>
LARSFitting.Estimators. betas()
Gets the sequence of betas.Constructors in dev.nm.stat.regression.linear.lasso.lars with parameters of type Vector Constructor Description LARSProblem(Vector y, Matrix X)
Constructs a LASSO variation of the Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.LARSProblem(Vector y, Matrix X, boolean lasso)
Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model and the covariates are normalized first.LARSProblem(Vector y, Matrix X, boolean normalized, boolean lasso)
Constructs a Least Angel Regression (LARS) problem, where an intercept is included in the model.LARSProblem(Vector y, Matrix X, boolean intercept, boolean normalized, boolean lasso)
Constructs a Least Angel Regression (LARS) problem. -
Uses of Vector in dev.nm.stat.regression.linear.logistic
Methods in dev.nm.stat.regression.linear.logistic with parameters of type Vector Modifier and Type Method Description double
LogisticRegression. Ey(Vector x)
Calculates the probability of occurrence (y = 1).Constructors in dev.nm.stat.regression.linear.logistic with parameters of type Vector Constructor Description LogisticBeta(Vector betaHat, LogisticResiduals residuals)
Construct an instance ofBeta
. -
Uses of Vector in dev.nm.stat.regression.linear.ols
Methods in dev.nm.stat.regression.linear.ols with parameters of type Vector Modifier and Type Method Description double
OLSRegression. Ey(Vector x)
static double
OLSRegression. Ey(Vector beta, Vector x, boolean intercept)
Constructors in dev.nm.stat.regression.linear.ols with parameters of type Vector Constructor Description OLSBeta(Vector betaHat, OLSResiduals residuals)
Constructs an instance ofBeta
.OLSResiduals(LMProblem problem, Vector betaHat)
Performs the residual analysis for an ordinary linear regression problem. -
Uses of Vector in dev.nm.stat.regression.linear.residualanalysis
Constructors in dev.nm.stat.regression.linear.residualanalysis with parameters of type Vector Constructor Description LMResiduals(LMProblem problem, Vector fitted)
Performs residual analysis for a linear regression problem. -
Uses of Vector in dev.nm.stat.stochasticprocess.multivariate.random
Methods in dev.nm.stat.stochasticprocess.multivariate.random that return Vector Modifier and Type Method Description protected Vector
MultivariateRandomProcess. dB(double dt)
Get a Brownian motion increment.protected Vector
MultivariateRandomProcess. Zt()
Get a d-dimensional Gaussian innovation.Constructors in dev.nm.stat.stochasticprocess.multivariate.random with parameters of type Vector Constructor Description MultivariateBrownianRRG(int d, TimeGrid timePoints, Vector initial)
Construct a random realization generator to produce multi-dimensional Brownian paths at time points specified.MultivariateRandomRealizationOfRandomProcess(MultivariateDiscreteSDE sde, TimeGrid timeGrid, Vector x0)
Construct a random realization generator from a multivariate discrete SDE.MultivariateRandomRealizationOfRandomProcess(MultivariateSDE sde, TimeGrid timeGrid, Vector x0)
Construct a random realization generator from a multivariate SDE.MultivariateRandomWalk(MultivariateDiscreteSDE sde, TimeGrid timeGrid, Vector x0)
Construct a multivariate stochastic process from an SDE. -
Uses of Vector in dev.nm.stat.stochasticprocess.multivariate.sde
Methods in dev.nm.stat.stochasticprocess.multivariate.sde that return Vector Modifier and Type Method Description Vector
MultivariateFt. dWt()
Get the increment of the driving Brownian motion during the time differential.Vector
FtAdaptedVectorFunction. evaluate(MultivariateFt ft)
Evaluate this function, f, at time t.Vector
MultivariateFtWt. Wt()
Get the current value(s) of the driving Brownian motion(s).Vector
MultivariateFt. Xt()
Get the current value of the stochastic process.Vector
MultivariateFt. Zt()
Get the current value of the Gaussian innovation.Methods in dev.nm.stat.stochasticprocess.multivariate.sde with parameters of type Vector Modifier and Type Method Description void
MultivariateFt. setXt(Vector Xt)
Set the current value of the stochastic process.void
MultivariateFt. setZt(Vector Zt)
Set the current value of the Gaussian innovation.void
MultivariateFtWt. setZt(Vector Zt)
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Uses of Vector in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
Methods in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients that return Vector Modifier and Type Method Description Vector
ConstantDriftVector. evaluate(MultivariateFt ft)
Vector
ZeroDriftVector. evaluate(MultivariateFt ft)
Constructors in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients with parameters of type Vector Constructor Description ConstantDriftVector(Vector mu)
Construct a constant drift function. -
Uses of Vector in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
Methods in dev.nm.stat.stochasticprocess.multivariate.sde.discrete that return Vector Modifier and Type Method Description Vector
MultivariateBrownianSDE. dXt(MultivariateFt ft)
Vector
MultivariateDiscreteSDE. dXt(MultivariateFt ft)
This is the SDE specification of a stochastic process.Vector
MultivariateEulerSDE. dXt(MultivariateFt ft)
This is the SDE specification of a stochastic process.Constructors in dev.nm.stat.stochasticprocess.multivariate.sde.discrete with parameters of type Vector Constructor Description MultivariateBrownianSDE(Vector mu, Matrix sigma)
Construct a multi-dimensional Brownian motion. -
Uses of Vector in dev.nm.stat.test.regression.linear.heteroskedasticity
Methods in dev.nm.stat.test.regression.linear.heteroskedasticity with parameters of type Vector Modifier and Type Method Description protected OLSRegression
Heteroskedasticity. getAuxiliaryOLSRegression(Vector y, LMResiduals residuals)
Get the auxiliary regression.protected OLSRegression
White. getAuxiliaryOLSRegression(Vector y, LMResiduals residuals)
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Uses of Vector in dev.nm.stat.timeseries.datastructure.multivariate
Methods in dev.nm.stat.timeseries.datastructure.multivariate that return Vector Modifier and Type Method Description Vector
MultivariateGenericTimeTimeSeries. get(int i)
Get the i-th value.Vector
MultivariateTimeSeries.Entry. getValue()
Vector[]
MultivariateGenericTimeTimeSeries. toArray()
Convert this multivariate time series into an array of vectors.Constructors in dev.nm.stat.timeseries.datastructure.multivariate with parameters of type Vector Constructor Description Entry(T time, Vector value)
Construct an instance ofTimeSeries.Entry
.MultivariateGenericTimeTimeSeries(T[] timestamps, Vector[] values)
Construct a multivariate time series from timestamps and vectors. -
Uses of Vector in dev.nm.stat.timeseries.datastructure.multivariate.realtime
Constructors in dev.nm.stat.timeseries.datastructure.multivariate.realtime with parameters of type Vector Constructor Description Entry(double time, Vector value)
Construct an instance ofTimeSeries.Entry
. -
Uses of Vector in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
Methods in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime that return Vector Modifier and Type Method Description Vector
MultivariateIntTimeTimeSeries. get(int t)
Get the value at timet
(random access).Vector
MultivariateSimpleTimeSeries. get(int t)
Constructors in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime with parameters of type Vector Constructor Description Entry(int time, Vector value)
Construct an instance ofEntry
.MultivariateSimpleTimeSeries(Vector... values)
Construct an instance ofMultivariateSimpleTimeSeries
. -
Uses of Vector in dev.nm.stat.timeseries.datastructure.univariate
Methods in dev.nm.stat.timeseries.datastructure.univariate that return Vector Modifier and Type Method Description static Vector
UnivariateTimeSeriesUtils. toVector(UnivariateTimeSeries<?,?> ts)
Cast a time series into a vector, discarding the timestamps. -
Uses of Vector in dev.nm.stat.timeseries.linear.multivariate.arima
Constructors in dev.nm.stat.timeseries.linear.multivariate.arima with parameters of type Vector Constructor Description VARIMAModel(Vector mu, Matrix[] phi, int d, Matrix[] theta)
Construct a multivariate ARIMA model with unit variance.VARIMAModel(Vector mu, Matrix[] phi, int d, Matrix[] theta, Matrix sigma)
Construct a multivariate ARIMA model.VARIMASim(VARIMAModel arima, Vector[] lags, Vector[] innovations, RandomVectorGenerator rvg)
Construct a multivariate ARIMA model.VARIMAXModel(Vector mu, Matrix[] phi, int d, Matrix[] theta, Matrix psi)
Construct a multivariate ARIMAX model with unit variance.VARIMAXModel(Vector mu, Matrix[] phi, int d, Matrix[] theta, Matrix psi, Matrix sigma)
Construct a multivariate ARIMAX model. -
Uses of Vector in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
Methods in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma that return Vector Modifier and Type Method Description Vector
VARMAModel. conditionalMean(Matrix arLags, Matrix maLags)
Compute the multivariate ARMA conditional mean, given all the lags.Vector
VARMAModel. unconditionalMean()
Compute the multivariate ARMA unconditional mean.Methods in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma with parameters of type Vector Modifier and Type Method Description Matrix
VARMAXModel. armaxMean(Matrix arLags, Matrix maLags, Vector exVar)
Compute the multivariate ARMAX conditional mean.Constructors in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma with parameters of type Vector Constructor Description VARMAModel(Vector mu, Matrix[] phi, Matrix[] theta)
Construct a multivariate ARMA model with unit variance.VARMAModel(Vector mu, Matrix[] phi, Matrix[] theta, Matrix sigma)
Construct a multivariate ARMA model.VARMAXModel(Vector mu, Matrix[] phi, Matrix[] theta, Matrix psi)
Construct a multivariate ARMAX model with unit variance.VARMAXModel(Vector mu, Matrix[] phi, Matrix[] theta, Matrix psi, Matrix sigma)
Construct a multivariate ARMAX model.VARModel(Vector mu, Matrix[] phi)
Construct a VAR model with unit variance.VARModel(Vector mu, Matrix[] phi, Matrix sigma)
Construct a VAR model.VARXModel(Vector mu, Matrix[] phi, Matrix psi)
Construct a VARX model with unit variance.VARXModel(Vector mu, Matrix[] phi, Matrix psi, Matrix sigma)
Construct a VARX model.VECM(Vector mu, Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a VECM(p) model.VECMLongrun(Vector mu, Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a long-run VECM(p) model.VECMTransitory(Vector mu, Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a transitory VECM(p) model.VMAModel(Vector mu, Matrix[] theta)
Construct a multivariate MA model with unit variance.VMAModel(Vector mu, Matrix[] theta, Matrix sigma)
Construct a multivariate MA model. -
Uses of Vector in dev.nm.stat.timeseries.linear.univariate.arima
Methods in dev.nm.stat.timeseries.linear.univariate.arima that return Vector Modifier and Type Method Description Vector
ARIMAForecastMultiStep. allForecasts()
Gets all the predictions of the next h steps in one vector.Vector
ARIMAForecastMultiStep. allMSEs()
Gets all the mean squared errors (MSE) of the h-step ahead predictions. -
Uses of Vector in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Methods in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that return Vector Modifier and Type Method Description Vector
ARMAForecastMultiStep. allForecasts()
Gets all the predictions of the next h steps in one vector.Vector
ARMAForecastMultiStep. allMSEs()
Gets all the mean squared errors (MSE) of the h-step ahead predictions.Vector
ARMAFit. stderr()
Get the asymptotic standard errors of the estimators. -
Uses of Vector in tech.nmfin.meanreversion.cointegration
Fields in tech.nmfin.meanreversion.cointegration declared as Vector Modifier and Type Field Description protected Vector
TradingPair. price1
protected Vector
TradingPair. price2
protected Vector
TradingPair. spread
Methods in tech.nmfin.meanreversion.cointegration that return Vector Modifier and Type Method Description Vector
TradingPair. spread()
S = A - bBMethods in tech.nmfin.meanreversion.cointegration with parameters of type Vector Modifier and Type Method Description TradingPair
RobustCointegration. computeRobustPair(String symbol1, String symbol2, Vector price1, Vector price2)
static Matrix
PairingModelUtils. getPriceMatrix(Vector p1, Vector p2)
Constructors in tech.nmfin.meanreversion.cointegration with parameters of type Vector Constructor Description TradingPair(String symbol1, String symbol2, Vector price1, Vector price2, double beta)
Constructs a related pair for trading, e.g., cointegrated pair. -
Uses of Vector in tech.nmfin.meanreversion.daspremont2008
Methods in tech.nmfin.meanreversion.daspremont2008 that return Vector Modifier and Type Method Description Vector
ExtremalGeneralizedEigenvalueByGreedySearch. computeOptimalPositions(int card)
Vector
ExtremalGeneralizedEigenvalueBySDP. computeOptimalPositions(int card)
Computes the solution to the problem described in Section 3.2 in reference.Vector
ExtremalGeneralizedEigenvalueSolver. computeOptimalPositions(int card)
Computes the solution to the problem described in Section 3.2 in reference.Vector
ExtremalGeneralizedEigenvalueByGreedySearch. getOptimalPositions(int minCard, int maxCard)
Methods in tech.nmfin.meanreversion.daspremont2008 with parameters of type Vector Modifier and Type Method Description double
ExtremalGeneralizedEigenvalueBySDP. getLambda(Vector x)
Computes the value of the objective function in eq. -
Uses of Vector in tech.nmfin.portfoliooptimization
Methods in tech.nmfin.portfoliooptimization that return Vector Modifier and Type Method Description default Vector
PortfolioOptimizationAlgorithm.MeanEstimator. getMeans(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Vector
PortfolioOptimizationAlgorithm.SampleMeanEstimator. getMeans(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Vector
Lai2010OptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Vector
PortfolioOptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Computes the optimal weights for the products using returns.Vector
TopNOptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
static Vector
TopNOptimizationAlgorithm. getTopN(Vector w0, int N, double B)
Methods in tech.nmfin.portfoliooptimization with parameters of type Vector Modifier and Type Method Description Vector
Lai2010OptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Vector
PortfolioOptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Computes the optimal weights for the products using returns.Vector
TopNOptimizationAlgorithm. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
static double
PortfolioUtils. getPortfolioReturns(Vector weights, Vector mu)
Computes the expected portfolio return.static double
PortfolioUtils. getPortfolioVariance(Vector weights, Matrix sigma)
Computes the portfolio variance.static double
PortfolioUtils. getSharpeRatio(Vector weights, Vector mu, Matrix sigma, double benchmarkRate)
Computes the portfolio Sharpe ratio.static Vector
TopNOptimizationAlgorithm. getTopN(Vector w0, int N, double B)
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Uses of Vector in tech.nmfin.portfoliooptimization.clm
Methods in tech.nmfin.portfoliooptimization.clm that return Vector Modifier and Type Method Description Vector
MarkowitzCriticalLine. getOptimalWeightForSetLambda(double lambda)
Vector
MCLNiedermayer. getOptimalWeightForSetLambda(double lambda)
Vector
MarkowitzCriticalLine. getOptimalWeightForTargetReturn(double target)
Vector
MCLNiedermayer. getOptimalWeightForTargetReturn(double target)
Vector
TurningPoint. weight()
Constructors in tech.nmfin.portfoliooptimization.clm with parameters of type Vector Constructor Description MCLNiedermayer(Vector gain, Matrix covariance)
Creates the critical line for given gain vector and covariance matrix, with non-negativity constraint.MCLNiedermayer(Vector gain, Matrix covariance, Vector lower, Vector upper)
Creates the critical line for given gain vector and covariance matrix, with given lower and upper bounds for weights. -
Uses of Vector in tech.nmfin.portfoliooptimization.corvalan2005
Methods in tech.nmfin.portfoliooptimization.corvalan2005 that return Vector Modifier and Type Method Description Vector
Corvalan2005. getDiversifiedWeights(Corvalan2005.WeightsConstraint constraint, Vector weights0, Matrix sigma, Vector r)
Finds the optimal weights for a diversified portfolio.Vector
Corvalan2005. getDiversifiedWeights(Corvalan2005.WeightsConstraint constraint, Vector weights0, Matrix sigma, Vector r, EqualityConstraints extraEqualityConstraints, LessThanConstraints extraLessThanConstraints)
Finds the optimal weights for a diversified portfolio.Methods in tech.nmfin.portfoliooptimization.corvalan2005 with parameters of type Vector Modifier and Type Method Description Vector
Corvalan2005. getDiversifiedWeights(Corvalan2005.WeightsConstraint constraint, Vector weights0, Matrix sigma, Vector r)
Finds the optimal weights for a diversified portfolio.Vector
Corvalan2005. getDiversifiedWeights(Corvalan2005.WeightsConstraint constraint, Vector weights0, Matrix sigma, Vector r, EqualityConstraints extraEqualityConstraints, LessThanConstraints extraLessThanConstraints)
Finds the optimal weights for a diversified portfolio.Constructor parameters in tech.nmfin.portfoliooptimization.corvalan2005 with type arguments of type Vector Constructor Description Corvalan2005(Minimizer<? super ConstrainedOptimProblem,IterativeSolution<Vector>> minimizer, DiversificationMeasure diversificationMeasure, double deltaSigma, double deltaR)
Constructs an instance of the Corvalan model. -
Uses of Vector in tech.nmfin.portfoliooptimization.corvalan2005.constraint
Constructors in tech.nmfin.portfoliooptimization.corvalan2005.constraint with parameters of type Vector Constructor Description MinimumWeights(Vector minWeights)
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Uses of Vector in tech.nmfin.portfoliooptimization.lai2010
Methods in tech.nmfin.portfoliooptimization.lai2010 that return Vector Modifier and Type Method Description Vector
Lai2010NPEBModel.OptimalWeights. weights()
Constructors in tech.nmfin.portfoliooptimization.lai2010 with parameters of type Vector Constructor Description OptimalWeights(Vector weights, Ceta.PortfolioMoments moments)
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Uses of Vector in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
Fields in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler declared as Vector Modifier and Type Field Description Vector
GARCHResamplerFactory2. initialReturns
Vector
GARCHResamplerFactory2. initialSigma2
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Uses of Vector in tech.nmfin.portfoliooptimization.lai2010.optimizer
Fields in tech.nmfin.portfoliooptimization.lai2010.optimizer declared as Vector Modifier and Type Field Description protected Vector
MVOptimizerMinWeights. w0
Methods in tech.nmfin.portfoliooptimization.lai2010.optimizer that return Vector Modifier and Type Method Description Vector
MVOptimizer. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Solves for the optimal weights given the moments, lambda, and eta.Vector
MVOptimizerLongOnly. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerMinWeights. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerNoConstraint. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerShrankMean. optimalWeights(Vector mu0, Matrix V, double lambda, double eta)
Methods in tech.nmfin.portfoliooptimization.lai2010.optimizer with parameters of type Vector Modifier and Type Method Description Vector
MVOptimizer. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Solves for the optimal weights given the moments, lambda, and eta.Vector
MVOptimizerLongOnly. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerMinWeights. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerNoConstraint. optimalWeights(Vector mu, Matrix V, double lambda, double eta)
Vector
MVOptimizerShrankMean. optimalWeights(Vector mu0, Matrix V, double lambda, double eta)
Constructors in tech.nmfin.portfoliooptimization.lai2010.optimizer with parameters of type Vector Constructor Description MVOptimizerMinWeights(Vector w0)
Constructs the solver with a constraint on the minimum weights (w ≥ w0). -
Uses of Vector in tech.nmfin.portfoliooptimization.markowitz
Methods in tech.nmfin.portfoliooptimization.markowitz that return Vector Modifier and Type Method Description protected Vector
MarkowitzByCLM. getOptimalW(double q)
protected Vector
MarkowitzByQP. getOptimalW(double q)
Solves w_eff = argmin {q * (w' Σ w) - w'r}.Vector
MarkowitzByQP. getOptimalWeights()
Gets the Markowitz optimal portfolio weights, for a given risk aversion coefficient.Constructors in tech.nmfin.portfoliooptimization.markowitz with parameters of type Vector Constructor Description MarkowitzByCLM(Vector r, Matrix V)
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ 0.MarkowitzByCLM(Vector r, Matrix V, Vector w_lower, Vector w_upper)
Solves w_eff = argmin {q * (w' V w) - w'r}, w'1 = 1, w ≥ w_lower, w ≤ w_upper.MarkowitzByCLM(Vector r, Matrix V, Vector w_lower, Vector w_upper, double benchmarkRate)
Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.MarkowitzByQP(Vector mu, Matrix sigma)
Constructs a Markowitz portfolio from expected future returns and future covariance, assuming no short selling constraint and zero benchmark rate.MarkowitzByQP(Vector mu, Matrix sigma, Vector lower, Vector upper)
Constructs a Markowitz portfolio from expected future returns and future covariance, with lower and upper limits on asset weights, assuming zero benchmark rate.MarkowitzByQP(Vector mu, Matrix sigma, Vector lower, Vector upper, double benchmarkRate)
Constructs a Markowitz portfolio from expected future returns and future covariance for a given benchmark rate, with lower and upper limits on asset weights.MarkowitzByQP(Vector mu, Matrix sigma, QPConstraint constraints)
Constructs a Markowitz portfolio from expected future returns and future covariance, assuming zero benchmark rate for Sharpe ratio calculation.MarkowitzByQP(Vector mu, Matrix sigma, QPConstraint constraints, double benchmarkRate)
Constructs a Markowitz portfolio from expected future returns and future covariance. -
Uses of Vector in tech.nmfin.portfoliooptimization.markowitz.constraints
Constructors in tech.nmfin.portfoliooptimization.markowitz.constraints with parameters of type Vector Constructor Description QPMinWeights(Vector minW)
QPWeightsLimit(Vector minW, Vector maxW)
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Uses of Vector in tech.nmfin.portfoliooptimization.nmsaam
Methods in tech.nmfin.portfoliooptimization.nmsaam that return Vector Modifier and Type Method Description Vector
NMSAAM. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Methods in tech.nmfin.portfoliooptimization.nmsaam with parameters of type Vector Modifier and Type Method Description Vector
NMSAAM. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
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Uses of Vector in tech.nmfin.portfoliooptimization.socp.constraints
Methods in tech.nmfin.portfoliooptimization.socp.constraints with parameters of type Vector Modifier and Type Method Description boolean
SOCPLinearBlackList. areAllConstraintsSatisfied(Vector y)
boolean
SOCPLinearMaximumLoan. areAllConstraintsSatisfied(Vector y)
boolean
SOCPLinearSectorNeutrality. areAllConstraintsSatisfied(Vector y)
boolean
SOCPLinearSelfFinancing. areAllConstraintsSatisfied(Vector y)
boolean
SOCPLinearZeroValue. areAllConstraintsSatisfied(Vector y)
boolean
SOCPMaximumLoan. areAllConstraintsSatisfied(Vector y)
boolean
SOCPNoTradingList1. areAllConstraintsSatisfied(Vector y)
boolean
SOCPSectorNeutrality. areAllConstraintsSatisfied(Vector y)
boolean
SOCPSelfFinancing. areAllConstraintsSatisfied(Vector y)
boolean
SOCPZeroValue. areAllConstraintsSatisfied(Vector y)
double
SOCPSectorNeutrality. bias(Vector y)
Computes the amount of deviation from neutrality, hence bias.double
SOCPSelfFinancing. bias(Vector y)
Computes the amount of deviation from self financing, hence bias.double
SOCPZeroValue. bias(Vector y)
Computes the amount of deviation from zero value, hence bias.Double
SOCPLinearBlackList. evaluate(Vector x)
Double
SOCPLinearMaximumLoan. evaluate(Vector y)
Double
SOCPLinearSectorNeutrality. evaluate(Vector y)
Double
SOCPLinearSelfFinancing. evaluate(Vector y)
Double
SOCPLinearZeroValue. evaluate(Vector y)
Double
SOCPMaximumLoan. evaluate(Vector y)
Double
SOCPNoTradingList1. evaluate(Vector x)
Note:x
here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.Double
SOCPSectorNeutrality. evaluate(Vector y)
Double
SOCPSelfFinancing. evaluate(Vector y)
Double
SOCPZeroValue. evaluate(Vector y)
Constructors in tech.nmfin.portfoliooptimization.socp.constraints with parameters of type Vector Constructor Description SOCPLinearMaximumLoan(Vector w0, Vector l)
Creates a maximum loan constraint.SOCPLinearMaximumLoan(Vector w0, Vector l, double epsilon)
Creates a maximum loan constraint.SOCPLinearSectorNeutrality(Vector[] S)
Creates a sector neutrality constraint.SOCPLinearSectorNeutrality(Vector[] S, double epsilon)
Creates a sector neutrality constraint.SOCPLinearSelfFinancing(Vector w0)
Creates a self financing constraint.SOCPLinearSelfFinancing(Vector w0, double epsilon)
Creates a self financing constraint.SOCPMaximumLoan(Vector w_0, Vector l)
Constructs a maximum loan constraint.SOCPMaximumLoan(Vector w_0, Vector l, double epsilon)
Constructs a maximum loan constraint.SOCPNoTradingList1(Vector w_0, Matrix D_BL0)
Constructs a black list constraint.SOCPNoTradingList1(Vector w_0, Matrix D_BL0, double epsilon)
Constructs a black list constraint.SOCPNoTradingList1(Vector w_0, Vector bl)
Constructs a black list constraint.SOCPSectorNeutrality(Vector w_0, Vector[] S)
Constructs a sector neutral constraint.SOCPSectorNeutrality(Vector w_0, Vector[] S, double epsilon)
Constructs a sector neutral constraint.SOCPSelfFinancing(Vector w_0)
Constructs a zero value constraint.SOCPSelfFinancing(Vector w_0, double epsilon)
Constructs a zero value constraint.SOCPZeroValue(Vector w_0)
Constructs a zero value constraint.SOCPZeroValue(Vector w_0, double epsilon)
Constructs a zero value constraint. -
Uses of Vector in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Methods in tech.nmfin.portfoliooptimization.socp.constraints.ybar with parameters of type Vector Modifier and Type Method Description boolean
SOCPLinearSectorExposure. areAllConstraintsSatisfied(Vector y)
boolean
SOCPNoTradingList2. areAllConstraintsSatisfied(Vector y)
boolean
SOCPSectorExposure. areAllConstraintsSatisfied(Vector y)
Double
SOCPLinearSectorExposure. evaluate(Vector y)
Double
SOCPNoTradingList2. evaluate(Vector x)
Note:x
here is the trading size, not the position. Evaluate the function f at x, where x is from the domain.Double
SOCPSectorExposure. evaluate(Vector y)
Constructors in tech.nmfin.portfoliooptimization.socp.constraints.ybar with parameters of type Vector Constructor Description SOCPLinearSectorExposure(Vector[] S, Vector u)
Creates a sector exposure constraint.SOCPLinearSectorExposure(Vector[] S, Vector u, double epsilon)
Creates a sector exposure constraint.SOCPNoTradingList2(Vector w_0, Matrix D_BL0)
Constructs a black list constraint.SOCPNoTradingList2(Vector w_0, Matrix D_BL0, double epsilon)
Constructs a black list constraint.SOCPSectorExposure(Vector w_0, Vector[] S, Vector u)
Constructs a sector exposure constraint.SOCPSectorExposure(Vector w_0, Vector[] S, Vector u, double epsilon)
Constructs a sector exposure constraint. -
Uses of Vector in tech.nmfin.returns
Methods in tech.nmfin.returns that return Vector Modifier and Type Method Description static Vector
Returns. getMeanReturns(double[][] returns)
Computes a vector of mean returns of the input returns (one column for one asset).static Vector
Returns. getMeanReturns(Matrix returns)
Computes a vector of mean returns of the input returns (one column for one asset). -
Uses of Vector in tech.nmfin.returns.moments
Methods in tech.nmfin.returns.moments that return Vector Modifier and Type Method Description Vector
ReturnsMoments. mu()
Gets the mean vector.Constructors in tech.nmfin.returns.moments with parameters of type Vector Constructor Description ReturnsMoments(Vector mu, Matrix V)
Constructs an instance. -
Uses of Vector in tech.nmfin.signal.infantino2010
Methods in tech.nmfin.signal.infantino2010 that return Vector Modifier and Type Method Description Vector
Infantino2010PCA.Signal. dReturns()
Gets the difference between predicted accumulated returns and realized accumulated returns at time T-H+1, the last historical fully realized accumulated return.Vector
Infantino2010PCA.Signal. realizedReturns(int t)
Gets the last H-period accumulated realized return.Vector
Infantino2010PCA.Signal. S_t_hat(int t)
Predicts the accumulated H-period return at time t.static Vector
Infantino2010PCA. sumUpLastRows(Matrix D, int lastIndex, int nRows)
Sums up, for each column, the lastnRows
rows.
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