Uses of Interface
dev.nm.algebra.linear.matrix.doubles.Matrix
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Uses of Matrix in dev.nm.algebra.linear.matrix.doubles
Classes in dev.nm.algebra.linear.matrix.doubles that implement Matrix Modifier and Type Class Description class
ImmutableMatrix
This is a read-only view of aMatrix
instance.Methods in dev.nm.algebra.linear.matrix.doubles that return Matrix Modifier and Type Method Description Matrix
ImmutableMatrix. add(Matrix that)
Matrix
MatrixRing. add(Matrix that)
this + thatMatrix
ImmutableMatrix. deepCopy()
Make a deep copy of the underlying matrix.Matrix
Matrix. deepCopy()
Matrix
ImmutableMatrix. minus(Matrix that)
Matrix
MatrixRing. minus(Matrix that)
this - thatMatrix
ImmutableMatrix. multiply(Matrix that)
Matrix
MatrixRing. multiply(Matrix that)
this * thatMatrix
ImmutableMatrix. ONE()
Matrix
MatrixRing. ONE()
Get an identity matrix that has the same dimension as this matrix.Matrix
ImmutableMatrix. opposite()
Matrix
MatrixRing. opposite()
Get the opposite of this matrix.Matrix
ImmutableMatrix. scaled(double c)
Matrix
Matrix. scaled(double c)
Scale this matrix, A, by a constant.Matrix
ImmutableMatrix. t()
Matrix
MatrixRing. t()
Get the transpose of this matrix.Matrix
ImmutableMatrix. ZERO()
Matrix
MatrixRing. ZERO()
Get a zero matrix that has the same dimension as this matrix.Methods in dev.nm.algebra.linear.matrix.doubles with parameters of type Matrix Modifier and Type Method Description Matrix
ImmutableMatrix. add(Matrix that)
Matrix
MatrixRing. add(Matrix that)
this + thatstatic boolean
MatrixPropertyUtils. areAllSparse(Matrix... matrices)
Checks if all matrices are SparseMatrix.static boolean
MatrixPropertyUtils. areEqual(Matrix A1, Matrix A2, double epsilon)
Checks the equality of two matrices up to a precision.static double
MatrixPropertyUtils. conditionNumber(Matrix A)
Computes the condition number of a given matrix A.static boolean
MatrixPropertyUtils. isDiagonal(Matrix A, double epsilon)
Checks if a square matrix is a diagonal matrix, up to a precision.static boolean
MatrixPropertyUtils. isIdempotent(Matrix A, double epsilon)
Checks if a matrix is idempotent.static boolean
MatrixPropertyUtils. isIdentity(Matrix A, double epsilon)
Checks if a matrix is an identity matrix, up to a precision.static boolean
MatrixPropertyUtils. isLowerBidiagonal(Matrix A, double epsilon)
Checks if a matrix is lower bidiagonal, up to a precision.static boolean
MatrixPropertyUtils. isLowerTriangular(Matrix A, double epsilon)
Checks if a matrix is lower triangular, up to a precision.static boolean
MatrixPropertyUtils. isMagicSquare(Matrix A)
Deprecated.Not supported yet.static boolean
MatrixPropertyUtils. isOrthogonal(Matrix A, double epsilon)
Checks if a matrix is orthogonal, up to a precision.static boolean
MatrixPropertyUtils. isPositiveDefinite(Matrix A, double epsilon)
Checks if a square matrix is positive definite; the matrix needs not be symmetric.static boolean
MatrixPropertyUtils. isPositiveSemiDefinite(Matrix A, double epsilon)
Checks if a square matrix is positive definite, up to a precision.static boolean
MatrixPropertyUtils. isQuasiTriangular(Matrix A, double epsilon)
Checks if a matrix is quasi (upper) triangular, up to a precision.static boolean
MatrixPropertyUtils. isReducedRowEchelonForm(Matrix A, double epsilon)
Checks if a matrix is in the reduced row echelon form, up to a precision.static boolean
MatrixPropertyUtils. isRowEchelonForm(Matrix A, double epsilon)
Checks if a matrix is in the row echelon form, up to a precision.static boolean
MatrixPropertyUtils. isScalar(Matrix A, double epsilon)
Deprecated.Not supported yet.static boolean
MatrixPropertyUtils. isSingular(Matrix A, double epsilon)
Checks if a square matrix is singular, i.e, having no inverse, up to a precision.static boolean
MatrixPropertyUtils. isSkewSymmetric(Matrix A, double epsilon)
Checks if a matrix is skew symmetric.static boolean
MatrixPropertyUtils. isSymmetric(Matrix A, double epsilon)
Checks if a matrix is symmetric.static boolean
MatrixPropertyUtils. isSymmetricPositiveDefinite(Matrix A, double epsilon)
Checks if a square matrix is symmetric and positive definite.static boolean
MatrixPropertyUtils. isTridiagonal(Matrix A, double epsilon)
Checks if a matrix is tridiagonal, up to a precision.static boolean
MatrixPropertyUtils. isUpperBidiagonal(Matrix A, double epsilon)
Checks if a matrix is upper bidiagonal, up to a precision.static boolean
MatrixPropertyUtils. isUpperTriangular(Matrix A, double epsilon)
Checks if a matrix is upper triangular, up to a precision.Matrix
ImmutableMatrix. minus(Matrix that)
Matrix
MatrixRing. minus(Matrix that)
this - thatMatrix
ImmutableMatrix. multiply(Matrix that)
Matrix
MatrixRing. multiply(Matrix that)
this * thatConstructors in dev.nm.algebra.linear.matrix.doubles with parameters of type Matrix Constructor Description ImmutableMatrix(Matrix A)
Construct a read-only version of a matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization that return Matrix Modifier and Type Method Description Matrix
SymmetricTridiagonalDecomposition. Q()
Returns the rotation matrix.Matrix
TriDiagonalization. Q()
Gets Q, such that Q * A * Q = T.Matrix
BiDiagonalization. U()
Gets U, where U' = Uk * ...Matrix
BiDiagonalizationByGolubKahanLanczos. U()
Matrix
BiDiagonalizationByHouseholder. U()
Gets U, where U' = Uk * ...Matrix
BiDiagonalization. V()
Gets V, where V' = Vk * ...Matrix
BiDiagonalizationByGolubKahanLanczos. V()
Matrix
BiDiagonalizationByHouseholder. V()
Gets V, where V' = Vk * ...Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.diagonalization with parameters of type Matrix Constructor Description BiDiagonalizationByGolubKahanLanczos(Matrix A)
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.BiDiagonalizationByGolubKahanLanczos(Matrix A, double epsilon, RandomLongGenerator rlg)
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.BiDiagonalizationByGolubKahanLanczos(Matrix A, RandomLongGenerator rlg)
Runs the Golub-Kahan-Lanczos bi-diagonalization for a tall matrix.BiDiagonalizationByHouseholder(Matrix A)
Runs the Householder bi-diagonalization for a tall matrix.SymmetricTridiagonalDecomposition(Matrix A)
Runs the tridiagonal decomposition for a square, symmetric matrix.TriDiagonalization(Matrix A)
Runs the tri-diagonalization process for a symmetric matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.eigen
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen that return Matrix Modifier and Type Method Description Matrix
EigenDecomposition. Q()
Get Q as in Q * D * Q' = A.Matrix
EigenDecomposition. Qt()
Get Q' as in Q * D * Q' = A.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.eigen with parameters of type Matrix Constructor Description CharacteristicPolynomial(Matrix A)
Construct the characteristic polynomial for a square matrix.Eigen(Matrix A)
Compute the eigenvalues and eigenvectors for a square matrix.Eigen(Matrix A, double epsilon)
UseEigen.Method.QR
method by default.Eigen(Matrix A, Eigen.Method method)
Compute the eigenvalues and eigenvectors for a square matrix.Eigen(Matrix A, Eigen.Method method, double epsilon)
Compute the eigenvalues and eigenvectors for a square matrix.EigenDecomposition(Matrix A)
Runs the eigen decomposition on a square matrix.EigenDecomposition(Matrix A, double epsilon)
Runs the eigen decomposition on a square matrix.InverseIteration(Matrix A, double lambda)
Construct an instance of InverseIteration to find the corresponding eigenvector.InverseIteration(Matrix A, double lambda, InverseIteration.StoppingCriterion criterion)
Construct an instance of InverseIteration to find the corresponding eigenvector. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 that return Matrix Modifier and Type Method Description Matrix
MR3. getEigenvectorMatrix()
Gets the eigenvector matrix, each column is an eigenvector.Matrix
SymmetricEigenByMR3. getEigenvectorMatrix()
Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.mr3 with parameters of type Matrix Constructor Description SymmetricEigenByMR3(Matrix A, boolean wantEigenvector)
Creates an instance for computing the eigen decomposition for a given symmetric matrix A.SymmetricEigenByMR3(Matrix A, boolean wantEigenvector, double epsilon)
Creates an instance for computing the eigen decomposition for a given symmetric matrix A. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr that return Matrix Modifier and Type Method Description Matrix
SymmetricQRAlgorithm. D()
Gets the D matrix as in the real Schur canonical form Q'AQ = D.Matrix
HessenbergDecomposition. H()
Gets the H matrix.Matrix
HessenbergDecomposition. Q()
Gets the Q matrix, where \[ Q = (Q_1 \times ...Matrix
QRAlgorithm. Q()
Gets the Q matrix as in the real Schur canonical form Q'AQ = T.Matrix
SymmetricQRAlgorithm. Q()
Gets the Q matrix as in Q'AQ = D, where D is diagonal and Q is orthogonal.Matrix
QRAlgorithm. T()
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr that return types with arguments of type Matrix Modifier and Type Method Description List<Matrix>
QRAlgorithm. Qs()
Gets the list of Qi's produced in the process of the QR algorithm (ifkeepQs
is set totrue
).List<Matrix>
Hessenberg. reduce(Matrix H)
Deprecated.Not supported yet.Methods in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr with parameters of type Matrix Modifier and Type Method Description Deflation
HessenbergDeflationSearch. backSearch(Matrix H)
Finds H22 such that H22 is the largest unreduced Hessenberg sub-matrix, and H33 is upper quasi-triangular.Deflation
TridiagonalDeflationSearch. backSearch(Matrix D)
Deflation
TridiagonalDeflationSearch. backSearch(Matrix D, int startPosition)
double
DefaultDeflationCriterion.MatrixNorm. compute(Matrix A)
static boolean
Hessenberg. isHessenberg(Matrix H, double epsilon)
Check if H is upper Hessenberg.boolean
DefaultDeflationCriterion. isNegligible(Matrix H, int i, int j, double epsilon)
Checks ifH[i,j]
is negligible by Steward's deflation criterion.boolean
DeflationCriterion. isNegligible(Matrix H, int i, int j, double epsilon)
Checks whether a sub-diagonal element is sufficiently small.boolean
Hessenberg. isReducible(Matrix H, double epsilon)
Check if H is upper Hessenberg and is reducible.List<Matrix>
Hessenberg. reduce(Matrix H)
Deprecated.Not supported yet.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.eigen.qr with parameters of type Matrix Constructor Description HessenbergDecomposition(Matrix A)
Runs the Hessenberg decomposition for a square matrix.QRAlgorithm(Matrix A)
Runs the QR algorithm on a square matrix.QRAlgorithm(Matrix A, double epsilon)
Runs the QR algorithm on a square matrix.QRAlgorithm(Matrix A, double epsilon, int maxIterations)
Runs the QR algorithm on a square matrix.SymmetricQRAlgorithm(Matrix A)
Runs the QR algorithm on a symmetric matrix.SymmetricQRAlgorithm(Matrix A, double epsilon)
Runs the QR algorithm on a symmetric matrix.SymmetricQRAlgorithm(Matrix A, double epsilon, int maxIterations)
Runs the QR algorithm on a symmetric matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination that return Matrix Modifier and Type Method Description Matrix
GaussianElimination. L()
Get the lower triangular matrix L, such that P * A = L * U.Matrix
GaussianElimination. T()
Get the transformation matrix, T, such that T * A = U.Matrix
GaussJordanElimination. T()
Get the transformation matrix, T, such that T * A = U.Matrix
GaussianElimination. U()
Get the upper triangular matrix, U, such that T * A = U and P * A = L * U.Matrix
GaussJordanElimination. U()
Get the reduced row echelon form matrix, U, such that T * A = U.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.gaussianelimination with parameters of type Matrix Constructor Description GaussianElimination(Matrix A)
Run the Gaussian elimination algorithm with partial pivoting.GaussianElimination(Matrix A, boolean usePivoting, double epsilon)
Run the Gaussian elimination algorithm.GaussianElimination4SquareMatrix(Matrix A)
Run the Gaussian elimination algorithm on a square matrix.GaussianElimination4SquareMatrix(Matrix A, double epsilon)
Run the Gaussian elimination algorithm on a square matrix.GaussJordanElimination(Matrix A)
Run the Gauss-Jordan elimination algorithm.GaussJordanElimination(Matrix A, boolean usePivoting, double epsilon)
Run the Gauss-Jordan elimination algorithm. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.qr
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.qr that return Matrix Modifier and Type Method Description Matrix
GramSchmidt. Q()
Matrix
HouseholderQR. Q()
Gets the Q matrix in the QR decomposition.Matrix
QR. Q()
Matrix
QRDecomposition. Q()
Get the orthogonal Q matrix in the QR decomposition, A = QR.Matrix
GramSchmidt. squareQ()
Get the square Q matrix.Matrix
HouseholderQR. squareQ()
Matrix
QR. squareQ()
Matrix
QRDecomposition. squareQ()
Get the square Q matrix.Matrix
GramSchmidt. tallR()
Matrix
HouseholderQR. tallR()
Matrix
QR. tallR()
Matrix
QRDecomposition. tallR()
Get the tall R matrix.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.qr with parameters of type Matrix Constructor Description GramSchmidt(Matrix A)
Run the Gram-Schmidt process to orthogonalize a matrix.GramSchmidt(Matrix A, boolean pad0Cols, double epsilon)
Run the Gram-Schmidt process to orthogonalize a matrix.HouseholderQR(Matrix A)
Runs the Householder reflection process to orthogonalize a matrix.HouseholderQR(Matrix A, double epsilon)
Runs the Householder reflection process to orthogonalize a matrix.QR(Matrix A)
Run the QR decomposition on a matrix.QR(Matrix A, double epsilon)
Run the QR decomposition on a matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.svd
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.svd that return Matrix Modifier and Type Method Description Matrix
GolubKahanSVD. U()
Matrix
SVD. U()
Matrix
SVDDecomposition. U()
Get the U matrix as in SVD decomposition.Matrix
GolubKahanSVD. Ut()
Matrix
SVD. Ut()
Matrix
SVDDecomposition. Ut()
Get the transpose of U, i.e.,U().t()
.Matrix
GolubKahanSVD. V()
Matrix
SVD. V()
Matrix
SVDDecomposition. V()
Get the V matrix as in SVD decomposition.Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.svd with parameters of type Matrix Constructor Description GolubKahanSVD(Matrix A, boolean doUV, boolean normalize, double epsilon)
Run the Golub-Kahan SVD decomposition on a tall matrix.GolubKahanSVD(Matrix A, boolean doUV, boolean normalize, double epsilon, int maxIterations)
Runs the Golub-Kahan SVD decomposition on a tall matrix.SVD(Matrix A, boolean doUV)
Runs the SVD decomposition on a matrix.SVD(Matrix A, boolean doUV, double epsilon)
Runs the SVD decomposition on a matrix.SVD(Matrix A, boolean doUV, double epsilon, SVD.Method method)
Runs the SVD decomposition on a matrix.SymmetricSVD(Matrix A)
Calculates the SVD of A.SymmetricSVD(Matrix A, double epsilon)
Calculates the SVD of A. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3
Methods in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3 that return Matrix Modifier and Type Method Description Matrix
BidiagonalSVDbyMR3. U()
Matrix
SVDbyMR3. U()
Matrix
BidiagonalSVDbyMR3. Ut()
Matrix
SVDbyMR3. Ut()
Matrix
BidiagonalSVDbyMR3. V()
Matrix
SVDbyMR3. V()
Matrix
BidiagonalSVDbyMR3. Vt()
Matrix
SVDbyMR3. Vt()
Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.svd.mr3 with parameters of type Matrix Constructor Description SVDbyMR3(Matrix A, boolean doUV)
Creates a singular value decomposition for a matrix A. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.triangle
Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.triangle with parameters of type Matrix Constructor Description Doolittle(Matrix A)
Runs Doolittle algorithm on a square matrix for LU decomposition.Doolittle(Matrix A, boolean usePivoting)
Runs Doolittle algorithm on a square matrix for LU decomposition.Doolittle(Matrix A, boolean usePivoting, double epsilon)
Runs Doolittle algorithm on a square matrix for LU decomposition.Doolittle(Matrix A, double epsilon)
Runs Doolittle algorithm on a square matrix for LU decomposition.LDLt(Matrix A)
Run the LDL decomposition on a real and symmetric (hence square) matrix.LDLt(Matrix A, double epsilon)
Run the LDL decomposition on a real and symmetric (hence square) matrix.LU(Matrix A)
Run the LU decomposition on a square matrix.LU(Matrix A, double epsilon)
Run the LU decomposition on a square matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky
Constructors in dev.nm.algebra.linear.matrix.doubles.factorization.triangle.cholesky with parameters of type Matrix Constructor Description Chol(Matrix A)
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.Chol(Matrix A, boolean isSymmetric)
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.Chol(Matrix A, double epsilon)
Run the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.CholeskyBanachiewicz(Matrix A)
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.CholeskyBanachiewiczParallelized(Matrix A)
CholeskySparse(Matrix A)
Runs the Cholesky decomposition on a real, symmetric (hence square), and positive definite matrix.CholeskyWang2006(Matrix Sigma, double epsilon)
Constructs the Cholesky decomposition of a matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.linearsystem
Methods in dev.nm.algebra.linear.matrix.doubles.linearsystem that return Matrix Modifier and Type Method Description Matrix
BackwardSubstitution. solve(UpperTriangularMatrix U, Matrix B)
Matrix
BackwardSubstitution. solve(UpperTriangularMatrix U, Matrix B, double epsilon)
Matrix
ForwardSubstitution. solve(LowerTriangularMatrix L, Matrix B)
Matrix
ForwardSubstitution. solve(LowerTriangularMatrix L, Matrix B, double epsilon)
Matrix
LUSolver. solve(Matrix A, Matrix B)
Solves AX = B.Matrix
Kernel. T()
Get the transformation matrix, T, such that T * A = U.Matrix
Kernel. U()
Get the upper triangular matrix U, such that T * A = U.Methods in dev.nm.algebra.linear.matrix.doubles.linearsystem with parameters of type Matrix Modifier and Type Method Description Matrix
BackwardSubstitution. solve(UpperTriangularMatrix U, Matrix B)
Matrix
BackwardSubstitution. solve(UpperTriangularMatrix U, Matrix B, double epsilon)
Matrix
ForwardSubstitution. solve(LowerTriangularMatrix L, Matrix B)
Matrix
ForwardSubstitution. solve(LowerTriangularMatrix L, Matrix B, double epsilon)
LinearSystemSolver.Solution
LinearSystemSolver. solve(Matrix A0)
Get a particular solution for the linear system, Ax = b.Matrix
LUSolver. solve(Matrix A, Matrix B)
Solves AX = B.Constructors in dev.nm.algebra.linear.matrix.doubles.linearsystem with parameters of type Matrix Constructor Description Kernel(Matrix A)
Construct the kernel of a matrix.Kernel(Matrix A, Kernel.Method method, double epsilon)
Construct the kernel of a matrix.LSProblem(Matrix A, Vector b)
Constructs a system of linear equations Ax = b. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype
Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype that implement Matrix Modifier and Type Class Description class
GivensMatrix
Givens rotation is a rotation in the plane spanned by two coordinates axes.class
HilbertMatrix
A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)class
PermutationMatrix
A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype that return Matrix Modifier and Type Method Description Matrix
GivensMatrix. add(Matrix that)
Matrix
PermutationMatrix. add(Matrix that)
Matrix
GivensMatrix. minus(Matrix that)
Matrix
PermutationMatrix. minus(Matrix that)
Matrix
GivensMatrix. multiply(Matrix A)
Left multiplication by G, namely, G * A.Matrix
PermutationMatrix. multiply(Matrix A)
Left multiplication by P.Matrix
GivensMatrix. multiplyInPlace(Matrix A)
Left multiplication by G, namely, G * A.Matrix
GivensMatrix. opposite()
Matrix
PermutationMatrix. opposite()
static Matrix
GivensMatrix. product(GivensMatrix[] Gs)
Given an array of Givens matrices {Gi}, computes G, where G = G1 * G2 * ...static Matrix
GivensMatrix. product(List<GivensMatrix> Gs)
Matrix
GivensMatrix. rightMultiply(Matrix A)
Right multiplication by G, namely, A * G.Matrix
PermutationMatrix. rightMultiply(Matrix A)
Right multiplication by P.Matrix
GivensMatrix. rightMultiplyInPlace(Matrix A)
Right multiplication by G, namely, A * G.Matrix
GivensMatrix. scaled(double c)
Matrix
PermutationMatrix. scaled(double scalar)
Matrix
GivensMatrix. ZERO()
Deprecated.no zero matrix for GivensMatrixMatrix
PermutationMatrix. ZERO()
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype with parameters of type Matrix Modifier and Type Method Description Matrix
GivensMatrix. add(Matrix that)
Matrix
PermutationMatrix. add(Matrix that)
static GivensMatrix
GivensMatrix. CtorToZeroOutEntry(Matrix A, int i, int j)
Constructs a Givens matrix such that G * A has 0 in the [i,j] entry.static GivensMatrix
GivensMatrix. CtorToZeroOutEntryByTranspose(Matrix A, int i, int j)
Constructs a Givens matrix such that Gt * A has 0 in the [i,j] entry.Matrix
GivensMatrix. minus(Matrix that)
Matrix
PermutationMatrix. minus(Matrix that)
Matrix
GivensMatrix. multiply(Matrix A)
Left multiplication by G, namely, G * A.Matrix
PermutationMatrix. multiply(Matrix A)
Left multiplication by P.Matrix
GivensMatrix. multiplyInPlace(Matrix A)
Left multiplication by G, namely, G * A.Matrix
GivensMatrix. rightMultiply(Matrix A)
Right multiplication by G, namely, A * G.Matrix
PermutationMatrix. rightMultiply(Matrix A)
Right multiplication by P.Matrix
GivensMatrix. rightMultiplyInPlace(Matrix A)
Right multiplication by G, namely, A * G. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense
Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense that implement Matrix Modifier and Type Class Description class
DenseMatrix
This class implements the standard, dense,double
based matrix representation.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense that return Matrix Modifier and Type Method Description Matrix
DenseMatrix. add(Matrix that)
Matrix
DenseMatrix. minus(Matrix that)
Matrix
DenseMatrix. multiply(Matrix that)
Matrix
DenseMatrix. opposite()
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense with parameters of type Matrix Modifier and Type Method Description Matrix
DenseMatrix. add(Matrix that)
Matrix
DenseMatrix. minus(Matrix that)
Matrix
DenseMatrix. multiply(Matrix that)
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense with parameters of type Matrix Constructor Description DenseMatrix(Matrix A)
Converts any matrix to the standard matrix representation. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal
Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal that implement Matrix Modifier and Type Class Description class
BidiagonalMatrix
A bi-diagonal matrix is either upper or lower diagonal.class
DiagonalMatrix
A diagonal matrix has non-zero entries only on the main diagonal.class
TridiagonalMatrix
A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal that return Matrix Modifier and Type Method Description Matrix
BidiagonalMatrix. add(Matrix that)
Matrix
DiagonalMatrix. add(Matrix that)
Computes the sum of two diagonal matrices.Matrix
TridiagonalMatrix. add(Matrix that)
Matrix
BidiagonalMatrix. minus(Matrix that)
Matrix
DiagonalMatrix. minus(Matrix that)
Computes the difference between two diagonal matrices.Matrix
TridiagonalMatrix. minus(Matrix that)
Matrix
BidiagonalMatrix. multiply(Matrix that)
this * thatMatrix
DiagonalMatrix. multiply(Matrix that)
Computes the product of two diagonal matrices.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal with parameters of type Matrix Modifier and Type Method Description Matrix
BidiagonalMatrix. add(Matrix that)
Matrix
DiagonalMatrix. add(Matrix that)
Computes the sum of two diagonal matrices.Matrix
TridiagonalMatrix. add(Matrix that)
Matrix
BidiagonalMatrix. minus(Matrix that)
Matrix
DiagonalMatrix. minus(Matrix that)
Computes the difference between two diagonal matrices.Matrix
TridiagonalMatrix. minus(Matrix that)
Matrix
BidiagonalMatrix. multiply(Matrix that)
this * thatMatrix
DiagonalMatrix. multiply(Matrix that)
Computes the product of two diagonal matrices.Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.diagonal with parameters of type Matrix Constructor Description TridiagonalMatrix(Matrix A)
Casts a matrix to tridiagonal by copying the 3 diagonals (ignoring all other entries). -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle
Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle that implement Matrix Modifier and Type Class Description class
LowerTriangularMatrix
A lower triangular matrix has 0 entries where column index > row index.class
SymmetricMatrix
A symmetric matrix is a square matrix such that its transpose equals to itself, i.e.,A[i][j] = A[j][i]
class
UpperTriangularMatrix
An upper triangular matrix has 0 entries where row index is greater than column index.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle that return Matrix Modifier and Type Method Description Matrix
LowerTriangularMatrix. add(Matrix that)
Matrix
SymmetricMatrix. add(Matrix that)
Matrix
UpperTriangularMatrix. add(Matrix that)
Matrix
LowerTriangularMatrix. minus(Matrix that)
Matrix
SymmetricMatrix. minus(Matrix that)
Matrix
UpperTriangularMatrix. minus(Matrix that)
Matrix
LowerTriangularMatrix. multiply(Matrix that)
Matrix
SymmetricMatrix. multiply(Matrix that)
Matrix
UpperTriangularMatrix. multiply(Matrix that)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle with parameters of type Matrix Modifier and Type Method Description Matrix
LowerTriangularMatrix. add(Matrix that)
Matrix
SymmetricMatrix. add(Matrix that)
Matrix
UpperTriangularMatrix. add(Matrix that)
Matrix
LowerTriangularMatrix. minus(Matrix that)
Matrix
SymmetricMatrix. minus(Matrix that)
Matrix
UpperTriangularMatrix. minus(Matrix that)
Matrix
LowerTriangularMatrix. multiply(Matrix that)
Matrix
SymmetricMatrix. multiply(Matrix that)
Matrix
UpperTriangularMatrix. multiply(Matrix that)
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.dense.triangle with parameters of type Matrix Constructor Description LowerTriangularMatrix(Matrix A)
Constructs a lower triangular matrix from a matrix.SymmetricMatrix(Matrix A)
Cast an (almost) symmetric matrix into SymmetricMatrix by averaging A(i,j) and A(j,i).SymmetricMatrix(Matrix A, boolean copyLower)
Cast an (almost) symmetric matrix into SymmetricMatrix.UpperTriangularMatrix(Matrix A)
Constructs an upper triangular matrix from a matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.mathoperation that return Matrix Modifier and Type Method Description Matrix
AutoParallelMatrixMathOperation. add(MatrixAccess A1, MatrixAccess A2)
Matrix
MatrixMathOperation. add(MatrixAccess A1, MatrixAccess A2)
A1 + A2Matrix
SimpleMatrixMathOperation. add(MatrixAccess A1, MatrixAccess A2)
Matrix
AutoParallelMatrixMathOperation. minus(MatrixAccess A1, MatrixAccess A2)
Matrix
MatrixMathOperation. minus(MatrixAccess A1, MatrixAccess A2)
A1 - A2Matrix
SimpleMatrixMathOperation. minus(MatrixAccess A1, MatrixAccess A2)
Matrix
AutoParallelMatrixMathOperation. multiply(MatrixAccess A1, MatrixAccess A2)
Matrix
MatrixMathOperation. multiply(MatrixAccess A1, MatrixAccess A2)
A1 * A2Matrix
SimpleMatrixMathOperation. multiply(MatrixAccess A1, MatrixAccess A2)
Matrix
AutoParallelMatrixMathOperation. scaled(MatrixAccess A, double c)
Matrix
MatrixMathOperation. scaled(MatrixAccess A, double c)
c * AMatrix
SimpleMatrixMathOperation. scaled(MatrixAccess A, double scalar)
Matrix
AutoParallelMatrixMathOperation. transpose(MatrixAccess A)
Matrix
MatrixMathOperation. transpose(MatrixAccess A)
Get the transpose of A.Matrix
SimpleMatrixMathOperation. transpose(MatrixAccess A)
-
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse
Subinterfaces of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse Modifier and Type Interface Description interface
SparseMatrix
A sparse matrix stores only non-zero values.Classes in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse that implement Matrix Modifier and Type Class Description class
CSRSparseMatrix
The Compressed Sparse Row (CSR) format for sparse matrix has this representation:(value, col_ind, row_ptr)
.class
DOKSparseMatrix
The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys.class
LILSparseMatrix
The list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value.Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse that return Matrix Modifier and Type Method Description Matrix
CSRSparseMatrix. add(Matrix that)
Matrix
DOKSparseMatrix. add(Matrix that)
Matrix
LILSparseMatrix. add(Matrix that)
Matrix
CSRSparseMatrix. minus(Matrix that)
Matrix
DOKSparseMatrix. minus(Matrix that)
Matrix
LILSparseMatrix. minus(Matrix that)
Matrix
CSRSparseMatrix. multiply(Matrix that)
Matrix
DOKSparseMatrix. multiply(Matrix that)
Matrix
LILSparseMatrix. multiply(Matrix that)
Methods in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse with parameters of type Matrix Modifier and Type Method Description Matrix
CSRSparseMatrix. add(Matrix that)
Matrix
DOKSparseMatrix. add(Matrix that)
Matrix
LILSparseMatrix. add(Matrix that)
Vector
SparseVector. leftMultiply(Matrix A)
Left multiplies a matrix.Matrix
CSRSparseMatrix. minus(Matrix that)
Matrix
DOKSparseMatrix. minus(Matrix that)
Matrix
LILSparseMatrix. minus(Matrix that)
Matrix
CSRSparseMatrix. multiply(Matrix that)
Matrix
DOKSparseMatrix. multiply(Matrix that)
Matrix
LILSparseMatrix. multiply(Matrix that)
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse with parameters of type Matrix Constructor Description CSRSparseMatrix(Matrix A)
Constructs a sparse matrix from a matrix. -
Uses of Matrix 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 with parameters of type Matrix Modifier and Type Method Description Preconditioner
PreconditionerFactory. newInstance(Matrix A)
Construct a new instance ofPreconditioner
for a coefficient matrix.Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner with parameters of type Matrix Constructor Description JacobiPreconditioner(Matrix A)
Construct a Jacobi preconditioner.SSORPreconditioner(Matrix A, double omega)
Construct an SSOR preconditioner with a symmetric coefficient matrix. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary
Constructors in dev.nm.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary with parameters of type Matrix 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 Matrix in dev.nm.algebra.linear.matrix.doubles.operation
Classes in dev.nm.algebra.linear.matrix.doubles.operation that implement Matrix Modifier and Type Class Description class
ColumnBindMatrix
A fast "cbind" matrix from vectors.class
CongruentMatrix
Given a matrix A and an invertible matrix P, we create the congruent matrix B s.t., B = P'APclass
DiagonalSum
Add diagonal elements to a matrix, an efficient implementation.class
FastKroneckerProduct
This is a fast and memory-saving implementation of computing the Kronecker product.class
Inverse
For a square matrix A, the inverse, A-1, if exists, satisfiesA.multiply(A.inverse()) == A.ONE()
There are multiple ways to compute the inverse of a matrix.class
KroneckerProduct
Given an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, is an (mp)-by-(nq) matrix with entries defined by cst = aij bkl whereclass
MAT
MAT
is the inverse operator ofSVEC
.class
MatrixRootByDiagonalization
The square root of a matrix extends the notion of square root from numbers to matrices.class
OuterProduct
The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.class
Pow
This is a square matrix A to the power of an integer n, An.class
PseudoInverse
The Moore-Penrose pseudo-inverse of an m x n matrix A is A+.class
SimilarMatrix
Given a matrix A and an invertible matrix P, we construct the similar matrix B s.t., B = P-1APclass
SubMatrixRef
This is a 'reference' to a sub-matrix of a larger matrix without copying it.class
SymmetricKronecker
Compute the symmetric Kronecker product of two matrices.Methods in dev.nm.algebra.linear.matrix.doubles.operation that return Matrix Modifier and Type Method Description Matrix
ColumnBindMatrix. add(Matrix that)
Matrix
DiagonalSum. add(Matrix that)
Matrix
FastKroneckerProduct. add(Matrix that)
Matrix
SubMatrixRef. add(Matrix that)
static Matrix
MatrixFactory. cbind(Matrix... matrices)
Combines an array of matrices by columns.static Matrix
MatrixFactory. cbind(Vector... vectors)
Combines an array of vectors by columns.static Matrix
MatrixFactory. cbind(List<Vector> vectors)
Combines a list of vectors by columns.static Matrix
MatrixFactory. columns(Matrix A, int[] cols)
Constructs a sub-matrix from the columns of a matrix.static Matrix
MatrixFactory. columns(Matrix A, int begin, int end)
Constructs a sub-matrix from the columns of a matrix.static Matrix
MatrixUtils. deColumnMean(Matrix A)
Get the de-mean (column means) matrix of a given matrix.Matrix
ColumnBindMatrix. deepCopy()
Matrix
DiagonalSum. deepCopy()
static Matrix
MatrixUtils. deRowMean(Matrix A)
Get the de-mean (row means) matrix of a given matrix.static Matrix
MatrixUtils. elementDivide(Matrix A1, Matrix A2)
static Matrix
MatrixUtils. elementMultiply(Matrix A1, Matrix A2)
static Matrix
MatrixUtils. elementOperation(Matrix A1, Matrix A2, BivariateRealFunction f)
static Matrix
MatrixFactory. foreach(Matrix A, UnivariateRealFunction f)
Constructs a new matrix in which each entry is the result of applying a function to the corresponding entry of a matrix.static Matrix
MatrixFactory. foreachColumn(Matrix matrix, RealVectorFunction f)
Constructs a new matrix in which each column is the result of applying a real vector function on each column vector of an input matrix.static Matrix
MatrixFactory. foreachRow(Matrix A, RealVectorFunction f)
Constructs a new matrix in which each row is the result of applying a real vector function on each row vector of an input matrix.static Matrix
MatrixFactory. identity(int nRows, int nCols)
Constructs a new identity matrix.static Matrix
MatrixFactory. minorMatrix(Matrix X, int row, int col)
Gets the minor matrix of a given matrix, by removing a specified row and a specified column.Matrix
ColumnBindMatrix. minus(Matrix that)
Matrix
DiagonalSum. minus(Matrix that)
Matrix
FastKroneckerProduct. minus(Matrix that)
Matrix
SubMatrixRef. minus(Matrix that)
Matrix
ColumnBindMatrix. multiply(Matrix that)
Matrix
DiagonalSum. multiply(Matrix that)
Matrix
FastKroneckerProduct. multiply(Matrix that)
Matrix
SubMatrixRef. multiply(Matrix that)
Matrix
ColumnBindMatrix. ONE()
Matrix
DiagonalSum. ONE()
Matrix
SubMatrixRef. ONE()
static Matrix
MatrixFactory. ones(int nRows, int nCols)
Constructs a matrix of 1's.static Matrix
MatrixFactory. ones(int nRows, int nCols, double s)
Constructs a matrix of the same scalar, e.g.,1.Matrix
ColumnBindMatrix. opposite()
Matrix
DiagonalSum. opposite()
Matrix
FastKroneckerProduct. opposite()
Matrix
SubMatrixRef. opposite()
static Matrix
MatrixFactory. randomPositiveDefiniteMatrix(int dim, RandomNumberGenerator rng)
Constructs a random symmetric, positive definite matrix.static Matrix
MatrixFactory. rbind(Matrix... matrices)
Combines an array of matrices by rows.static Matrix
MatrixFactory. rbind(Vector... vectors)
Combines an array of vectors by rows.static Matrix
MatrixFactory. rbind(List<Vector> vectors)
Combines a list of array of vectors by rows.static Matrix
MatrixFactory. replaceInPlace(Matrix original, int rowFrom, int rowTo, int colFrom, int colTo, Matrix replacement)
Replaces a sub-matrix of a matrix with a smaller matrix.static Matrix
MatrixFactory. rows(Matrix A, int[] rows)
Construct a sub-matrix from the rows of a matrix.static Matrix
MatrixFactory. rows(Matrix A, int begin, int end)
Constructs a sub-matrix from the rows of a matrix.Matrix
ColumnBindMatrix. scaled(double c)
Matrix
DiagonalSum. scaled(double c)
Matrix
FastKroneckerProduct. scaled(double scalar)
Matrix
SubMatrixRef. scaled(double scalar)
static Matrix
MatrixFactory. subMatrix(Matrix A, int[] rows, int[] cols)
Constructs a sub-matrix from the intersections of rows and columns of a matrix.static Matrix
MatrixFactory. subMatrix(Matrix A, int rowFrom, int rowTo, int colFrom, int colTo)
Constructs a sub-matrix from the four corners of a matrix.static Matrix
MatrixFactory. subMatrix(Matrix A, List<Integer> rows, List<Integer> cols)
Constructs a sub-matrix from the intersections of rows and columns of a matrix.Matrix
ColumnBindMatrix. t()
Matrix
DiagonalSum. t()
Matrix
FastKroneckerProduct. t()
Matrix
SubMatrixRef. t()
Matrix
ElementaryOperation. T()
Get the transformed matrix T.Matrix
ColumnBindMatrix. ZERO()
Matrix
DiagonalSum. ZERO()
Matrix
SubMatrixRef. ZERO()
Methods in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type Matrix Modifier and Type Method Description Matrix
ColumnBindMatrix. add(Matrix that)
Matrix
DiagonalSum. add(Matrix that)
Matrix
FastKroneckerProduct. add(Matrix that)
Matrix
SubMatrixRef. add(Matrix that)
static Matrix
MatrixFactory. cbind(Matrix... matrices)
Combines an array of matrices by columns.static Matrix
MatrixFactory. columns(Matrix A, int[] cols)
Constructs a sub-matrix from the columns of a matrix.static Matrix
MatrixFactory. columns(Matrix A, int begin, int end)
Constructs a sub-matrix from the columns of a matrix.static Matrix
MatrixUtils. deColumnMean(Matrix A)
Get the de-mean (column means) matrix of a given matrix.static Matrix
MatrixUtils. deRowMean(Matrix A)
Get the de-mean (row means) matrix of a given matrix.static double
MatrixMeasure. det(Matrix A)
Compute the determinant of a matrix.static DiagonalMatrix
MatrixFactory. diagonalMatrix(Matrix A)
Gets the diagonal of a matrix.static Matrix
MatrixUtils. elementDivide(Matrix A1, Matrix A2)
static Matrix
MatrixUtils. elementMultiply(Matrix A1, Matrix A2)
static Matrix
MatrixUtils. elementOperation(Matrix A1, Matrix A2, BivariateRealFunction f)
static Matrix
MatrixFactory. foreach(Matrix A, UnivariateRealFunction f)
Constructs a new matrix in which each entry is the result of applying a function to the corresponding entry of a matrix.static Matrix
MatrixFactory. foreachColumn(Matrix matrix, RealVectorFunction f)
Constructs a new matrix in which each column is the result of applying a real vector function on each column vector of an input matrix.static Matrix
MatrixFactory. foreachRow(Matrix A, RealVectorFunction f)
Constructs a new matrix in which each row is the result of applying a real vector function on each row vector of an input matrix.static double
MatrixMeasure. Frobenius(Matrix A)
Compute the Frobenius norm, i.e., the sqrt of the sum of squares of all elements of a matrix.static double
MatrixMeasure. max(Matrix A)
Compute the maximal entry in a matrix.static double
MatrixMeasure. min(Matrix A)
Compute the minimal entry in a matrix.static Matrix
MatrixFactory. minorMatrix(Matrix X, int row, int col)
Gets the minor matrix of a given matrix, by removing a specified row and a specified column.Matrix
ColumnBindMatrix. minus(Matrix that)
Matrix
DiagonalSum. minus(Matrix that)
Matrix
FastKroneckerProduct. minus(Matrix that)
Matrix
SubMatrixRef. minus(Matrix that)
Matrix
ColumnBindMatrix. multiply(Matrix that)
Matrix
DiagonalSum. multiply(Matrix that)
Matrix
FastKroneckerProduct. multiply(Matrix that)
Matrix
SubMatrixRef. multiply(Matrix that)
static int
MatrixMeasure. nullity(Matrix A)
Deprecated.Not supported yet.static int
MatrixMeasure. rank(Matrix A)
Compute the numerical rank of a matrix.static int
MatrixMeasure. rank(Matrix A, double epsilon)
Compute the numerical rank of a matrix.static Matrix
MatrixFactory. rbind(Matrix... matrices)
Combines an array of matrices by rows.static Matrix
MatrixFactory. replaceInPlace(Matrix original, int rowFrom, int rowTo, int colFrom, int colTo, Matrix replacement)
Replaces a sub-matrix of a matrix with a smaller matrix.static Matrix
MatrixFactory. rows(Matrix A, int[] rows)
Construct a sub-matrix from the rows of a matrix.static Matrix
MatrixFactory. rows(Matrix A, int begin, int end)
Constructs a sub-matrix from the rows of a matrix.static Matrix
MatrixFactory. subMatrix(Matrix A, int[] rows, int[] cols)
Constructs a sub-matrix from the intersections of rows and columns of a matrix.static Matrix
MatrixFactory. subMatrix(Matrix A, int rowFrom, int rowTo, int colFrom, int colTo)
Constructs a sub-matrix from the four corners of a matrix.static Matrix
MatrixFactory. subMatrix(Matrix A, List<Integer> rows, List<Integer> cols)
Constructs a sub-matrix from the intersections of rows and columns of a 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.static double
MatrixMeasure. tr(Matrix A)
Compute the sum of the diagonal elements, i.e., the trace of a matrix.Constructors in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type Matrix Constructor Description CongruentMatrix(Matrix P, Matrix A)
Constructs the congruent matrix B = P'AP.DiagonalSum(Matrix A, double d)
DiagonalSum(Matrix A, double[] d)
DiagonalSum(Matrix A, Vector d)
ElementaryOperation(Matrix A)
Transform A by elementary operations.FastKroneckerProduct(Matrix A, Matrix B)
Construct a Kronecker product for read-only.InnerProduct(Matrix A, Matrix B)
Compute the inner product of two matrices.Inverse(Matrix A)
Constructs the inverse of a matrix.Inverse(Matrix A, double epsilon)
Constructs the inverse of a matrix.KroneckerProduct(Matrix A, Matrix B)
Construct the Kronecker product of two matrices.MatrixRootByDiagonalization(Matrix S)
Constructs the square root of a Matrix by diagonalization.Pow(Matrix A, int n)
Construct the power matrix An so that An = (1e100)scale * BPow(Matrix A, int n, double base)
Construct the power matrix An so that An = basescale * BPseudoInverse(Matrix A)
Construct the Moore-Penrose pseudo-inverse matrix of A.PseudoInverse(Matrix A, double epsilon)
Construct the Moore-Penrose pseudo-inverse matrix of a matrix.SimilarMatrix(Matrix P, Matrix A)
Constructs the similar matrix B = P-1AP.SubMatrixRef(Matrix A)
Constructs a reference to the whole matrix.SubMatrixRef(Matrix A, int[] rows, int[] cols)
Constructs a sub-matrix reference.SubMatrixRef(Matrix A, int rowFrom, int rowTo, int colFrom, int colTo)
Constructs a sub-matrix reference.SVEC(Matrix A)
Construct the SVEC of a matrix.SymmetricKronecker(Matrix M, Matrix N)
Compute the symmetric Kronecker product of two matrices.VariancebtX(Vector b, Matrix X)
Computes \(b'Xb\). -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.operation.householder
Methods in dev.nm.algebra.linear.matrix.doubles.operation.householder that return Matrix Modifier and Type Method Description Matrix
HouseholderInPlace. getTransformedMatrix()
Gets the final matrix transformed by all the Householder transformations.Matrix
HouseholderReflection. H()
Get the Householder matrix H = I - 2 * v * v'.static Matrix
HouseholderReflection. product(HouseholderReflection[] Hs, int from, int to)
Compute Q from Householder matrices {Qi}.static Matrix
HouseholderReflection. product(HouseholderReflection[] Hs, int from, int to, int nRows, int nCols)
Compute Q from Householder matrices {Qi}.Matrix
HouseholderReflection. reflect(Matrix A)
Apply the Householder matrix, H, to a matrix (a set of column vectors), A.Matrix
HouseholderReflection. reflectColumns(Matrix A)
Matrix
HouseholderReflection. reflectRows(Matrix A)
Matrix
HouseholderReflection. rightReflect(Matrix A)
Apply the Householder matrix, H, to a matrix (a set of row vectors), A.Matrix
HouseholderInPlace. U()
Gets the accumulated Householder reflections applied to A.Matrix
HouseholderInPlace. Vt()
Gets the inverse (or transpose) of accumulated Householder right-reflections applied to A.Methods in dev.nm.algebra.linear.matrix.doubles.operation.householder with parameters of type Matrix Modifier and Type Method Description Matrix
HouseholderReflection. reflect(Matrix A)
Apply the Householder matrix, H, to a matrix (a set of column vectors), A.Matrix
HouseholderReflection. reflectColumns(Matrix A)
Matrix
HouseholderReflection. reflectRows(Matrix A)
Matrix
HouseholderReflection. rightReflect(Matrix A)
Apply the Householder matrix, H, to a matrix (a set of row vectors), A.Constructors in dev.nm.algebra.linear.matrix.doubles.operation.householder with parameters of type Matrix Constructor Description HouseholderInPlace(Matrix A)
Creates an instance that transforms the given matrix A.HouseholderInPlace(Matrix A, double epsilon)
Creates an instance that transforms the given matrix A. -
Uses of Matrix in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite
Classes in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite that implement Matrix Modifier and Type Class Description class
GoldfeldQuandtTrotter
Goldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.class
MatthewsDavies
Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.class
PositiveDefiniteMatrixByPositiveDiagonal
This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.class
PositiveSemiDefiniteMatrixNonNegativeDiagonal
This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by forcing the negative diagonal entries in the eigen decomposition to 0.Constructors in dev.nm.algebra.linear.matrix.doubles.operation.positivedefinite with parameters of type Matrix Constructor Description GoldfeldQuandtTrotter(Matrix H, double beta)
Constructs a symmetric, positive definite matrix using the Goldfeld-Quandt-Trotter algorithm.MatthewsDavies(Matrix H)
Constructs a symmetric, positive definite matrix using the Matthews-Davies algorithm.PositiveDefiniteMatrixByPositiveDiagonal(Matrix A, double epsilon, double small)
Constructs a positive definite matrix by forcing the diagonal entries in the eigen decomposition to a small non-negative number, e.g., 0.PositiveSemiDefiniteMatrixNonNegativeDiagonal(Matrix A, double epsilon)
Constructs a positive semi-definite matrix by forcing the negative diagonal entries in the eigen decomposition to 0. -
Uses of Matrix in dev.nm.algebra.linear.vector.doubles.dense
Constructors in dev.nm.algebra.linear.vector.doubles.dense with parameters of type Matrix Constructor Description DenseVector(Matrix A)
Constructs a vector from a column or row matrix. -
Uses of Matrix in dev.nm.algebra.linear.vector.doubles.operation
Methods in dev.nm.algebra.linear.vector.doubles.operation with parameters of type Matrix Modifier and Type Method Description static Vector
VectorFactory. diagonal(Matrix A)
Gets the diagonal of a matrix as 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. subDiagonal(Matrix A)
Gets the sub-diagonal of a matrix as a vector.static Vector
VectorFactory. superDiagonal(Matrix A)
Gets the super-diagonal of a matrix as a vector.Constructors in dev.nm.algebra.linear.vector.doubles.operation with parameters of type Matrix Constructor Description RealVectorSpace(Matrix A)
Construct a vector space from a matrix (a set of column vectors).RealVectorSpace(Matrix A, double epsilon)
Construct a vector space from a matrix (a set of column vectors). -
Uses of Matrix in dev.nm.analysis.differentiation.multivariate
Classes in dev.nm.analysis.differentiation.multivariate that implement Matrix Modifier and Type Class Description class
BorderedHessian
A bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g.class
Hessian
The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function.class
Jacobian
The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.Methods in dev.nm.analysis.differentiation.multivariate that return Matrix Modifier and Type Method Description Matrix
HessianFunction. evaluate(Vector x)
Matrix
JacobianFunction. evaluate(Vector x)
-
Uses of Matrix in dev.nm.analysis.function.matrix
Methods in dev.nm.analysis.function.matrix that return Matrix Modifier and Type Method Description abstract Matrix
R1toMatrix. evaluate(double x)
Evaluate f(x) = A.Matrix
R1toMatrix. evaluate(Vector x)
abstract Matrix
R2toMatrix. evaluate(double x1, double x2)
Evaluate f(x1, x2) = A.Matrix
R2toMatrix. evaluate(Vector x)
Constructors in dev.nm.analysis.function.matrix with parameters of type Matrix Constructor Description R1toConstantMatrix(Matrix A)
Construct a constant matrix function. -
Uses of Matrix in dev.nm.analysis.function.rn2r1
Constructors in dev.nm.analysis.function.rn2r1 with parameters of type Matrix 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 Matrix in dev.nm.solver.multivariate.constrained.constraint.linear
Methods in dev.nm.solver.multivariate.constrained.constraint.linear that return Matrix Modifier and Type Method Description Matrix
LinearConstraints. getActiveConstraints(Vector x, double epsilon)
Get the active constraint.Constructors in dev.nm.solver.multivariate.constrained.constraint.linear with parameters of type Matrix Constructor Description 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 Matrix in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing
Fields in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing declared as Matrix Modifier and Type Field Description protected Matrix
PrimalDualPathFollowingMinimizer.Solution. A
This is either [A] or [ A] [-C]protected Matrix
PrimalDualPathFollowingMinimizer.Solution. I
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing that return Matrix Modifier and Type Method Description Matrix
Hp. evaluate(Matrix U)
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).protected Matrix
CSDPMinimizer.Solution. svecA()
protected Matrix
HomogeneousPathFollowingMinimizer.Solution. svecA()
Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".protected Matrix
PrimalDualPathFollowingMinimizer.Solution. svecA()
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing with parameters of type Matrix Modifier and Type Method Description Matrix
Hp. evaluate(Matrix U)
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).protected static double
CSDPMinimizer. getMinEigenValue(Matrix A, double epsilon)
Gets the minimum of all the eigenvalues of a matrix.protected static double
HomogeneousPathFollowingMinimizer. getMinEigenValue(Matrix A, double epsilon)
Gets the minimum of all the eigenvalues of a matrix.protected static double
PrimalDualPathFollowingMinimizer. getMinEigenValue(Matrix A, double epsilon)
Gets the minimum of all the eigen values of a matrix.Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.pathfollowing with parameters of type Matrix Constructor Description CentralPath(Matrix X, Vector y, Matrix S)
Construct a central path.Hp(Matrix P)
Constructs a symmetrization operator. -
Uses of Matrix in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem that return Matrix Modifier and Type Method Description Matrix
SOCPDualProblem. A()
\[ A = [A_1, A_2, ...Matrix
SOCPDualProblem. A(int i)
Gets Ai.Matrix
SOCPGeneralConstraint. A()
Gets A.Matrix
SOCPLinearEquality. A()
Matrix
SOCPLinearInequality. A()
Matrix
SOCPDualProblem1. A_full()
Combine all A data as a matrix.Matrix
SOCPDualProblem1. A_l()
Matrix
SOCPDualProblem1. A_l_full()
Combine all linear blocks as a matrix.Matrix
SOCPDualProblem1. A_q(int i)
Gets \({A^q}_i\).Matrix
SOCPDualProblem1. A_q_full()
A^q = [{A^q}_1, {A^q}_2, ...Matrix
SOCPDualProblem1. A_u()
Constructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem with parameters of type Matrix 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.SOCPLinearEquality(Matrix A, Vector c)
SOCPLinearInequality(Matrix A, Vector c)
-
Uses of Matrix 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 Matrix Modifier and Type Method Description Matrix
PortfolioRiskExactSigma.DefaultRoot. getRoot(Matrix A)
Matrix
PortfolioRiskExactSigma.Diagonalization. getRoot(Matrix A)
Matrix
PortfolioRiskExactSigma.MatrixRoot. getRoot(Matrix A)
Gets the root of a matrixMatrix
PortfolioRiskExactSigma. root()
Matrix
PortfolioRiskExactSigma. Sigma()
abstract Matrix
SOCPRiskConstraint. Sigma()
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization with parameters of type Matrix Modifier and Type Method Description Matrix
PortfolioRiskExactSigma.DefaultRoot. getRoot(Matrix A)
Matrix
PortfolioRiskExactSigma.Diagonalization. getRoot(Matrix A)
Matrix
PortfolioRiskExactSigma.MatrixRoot. getRoot(Matrix A)
Gets the root of a matrixConstructors in dev.nm.solver.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization with parameters of type Matrix Constructor Description PortfolioRiskExactSigma(Matrix Sigma)
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.PortfolioRiskExactSigma(Matrix Sigma, Matrix sigmaRoot)
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.PortfolioRiskExactSigma(Matrix Sigma, PortfolioRiskExactSigma.MatrixRoot root)
Transforms the portfolio risk term, \(y^{\top}\Sigma\;y\leq t_1\), into the standard SOCP form when the exact covariance matrix is used.SOCPPortfolioObjectiveFunction(Matrix returns, double[] lambda, SOCPRiskConstraint risk, SOCPPortfolioConstraint impact)
Constructs the objective function for an SOCP portfolio optimization (minimization) problem.SOCPPortfolioObjectiveFunction(Matrix returns, double lambda, SOCPRiskConstraint risk)
Constructs the objective function for an SOCP portfolio optimization (minimization) problem without a market impact term. -
Uses of Matrix 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 Matrix Constructor Description LPCanonicalProblem1(Vector c, Matrix A, Vector b)
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. -
Uses of Matrix 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 with parameters of type Matrix 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 Matrix Constructor Description Problem(Matrix A, Vector b, Vector c)
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Uses of Matrix in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem
Methods in dev.nm.solver.multivariate.constrained.convex.sdp.socp.qp.problem that return Matrix Modifier and Type Method Description Matrix
QPProblemOnlyEqualityConstraints. getAplus()
Matrix
QPProblemOnlyEqualityConstraints. getVr()
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Uses of Matrix in dev.nm.solver.multivariate.constrained.general.sqp.activeset
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset that return Matrix Modifier and Type Method Description Matrix
SQPASVariation. getInitialHessian(Vector x0, Vector v0, Vector u0)
Get the initial Hessian matrix.Matrix
SQPASVariation1. getInitialHessian(Vector x0, Vector v0, Vector u0)
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.Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset with parameters of type Matrix Modifier and Type Method Description 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 Matrix in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint
Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint that return Matrix Modifier and Type Method Description 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.Methods in dev.nm.solver.multivariate.constrained.general.sqp.activeset.equalityconstraint with parameters of type Matrix Modifier and Type Method Description 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)
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Uses of Matrix in dev.nm.solver.multivariate.unconstrained.c2.quasinewton
Fields in dev.nm.solver.multivariate.unconstrained.c2.quasinewton declared as Matrix Modifier and Type Field Description protected Matrix
QuasiNewtonMinimizer.QuasiNewtonImpl. Sk
This is the approximate inverse of the Hessian matrix.Methods in dev.nm.solver.multivariate.unconstrained.c2.quasinewton that return Matrix Modifier and Type Method Description static Matrix
BFGSMinimizer. dampedBFGSHessianUpdate(Matrix H, Vector gamma, Vector delta)
Damped BFGS Hessian update.static Matrix
DFPMinimizer. updateHessianInverse(Matrix S, Matrix gamma, Matrix delta)
Sk+1 = Sk + δδ' / γ'δ - Sγγ'S' / γ'Sγstatic Matrix
BFGSMinimizer. updateHessianInverse1(Matrix S, Matrix gamma, Matrix delta)
Sk+1 = Sk + (1 + γ'Sγ/γ'δ)/γ'δ * δδ' -(δγ'S + Sγδ') / γ'δ, where S = H-1static Matrix
BFGSMinimizer. updateHessianInverse2(Matrix S, Matrix gamma, Matrix delta)
P + γγ' / γ'δ - P %*% γγ' %*% P / γ'Pδ, where P = S-1 is the Hessian.Methods in dev.nm.solver.multivariate.unconstrained.c2.quasinewton with parameters of type Matrix Modifier and Type Method Description static Matrix
BFGSMinimizer. dampedBFGSHessianUpdate(Matrix H, Vector gamma, Vector delta)
Damped BFGS Hessian update.static Matrix
DFPMinimizer. updateHessianInverse(Matrix S, Matrix gamma, Matrix delta)
Sk+1 = Sk + δδ' / γ'δ - Sγγ'S' / γ'Sγstatic Matrix
BFGSMinimizer. updateHessianInverse1(Matrix S, Matrix gamma, Matrix delta)
Sk+1 = Sk + (1 + γ'Sγ/γ'δ)/γ'δ * δδ' -(δγ'S + Sγδ') / γ'δ, where S = H-1static Matrix
BFGSMinimizer. updateHessianInverse2(Matrix S, Matrix gamma, Matrix delta)
P + γγ' / γ'δ - P %*% γγ' %*% P / γ'Pδ, where P = S-1 is the Hessian. -
Uses of Matrix in dev.nm.stat.cointegration
Methods in dev.nm.stat.cointegration that return Matrix Modifier and Type Method Description Matrix
CointegrationMLE. alpha()
Get the set of adjusting coefficients, by columns.Matrix
CointegrationMLE. beta()
Get the set of cointegrating factors, by columns.Constructors in dev.nm.stat.cointegration with parameters of type Matrix Constructor Description CointegrationMLE(MultivariateSimpleTimeSeries ts, boolean intercept, int p, Matrix D)
Perform the Johansen MLE procedure on a multivariate time series. -
Uses of Matrix in dev.nm.stat.covariance
Methods in dev.nm.stat.covariance that return Matrix Modifier and Type Method Description Matrix
LedoitWolf2004.Result. getCovarianceMatrix()
Gets the "shrunk" covariance matrix.Methods in dev.nm.stat.covariance with parameters of type Matrix Modifier and Type Method Description LedoitWolf2004.Result
LedoitWolf2004. compute(Matrix Y)
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the optimal shrinkage parameter computed by the algorithm.LedoitWolf2004.Result
LedoitWolf2004. compute(Matrix Y, double shrinkage)
Estimates the covariance matrix for a given matrix Y (each column in Y is a time-series), with the given shrinkage parameter. -
Uses of Matrix in dev.nm.stat.covariance.covarianceselection
Methods in dev.nm.stat.covariance.covarianceselection that return Matrix Modifier and Type Method Description Matrix
CovarianceSelectionSolver. covariance()
Get the estimated Covariance matrix of the selection problem.Matrix
CovarianceSelectionSolver. inverseCovariance()
Get the estimated inverse Covariance matrix of the selection problem.Methods in dev.nm.stat.covariance.covarianceselection with parameters of type Matrix Modifier and Type Method Description double
CovarianceSelectionProblem. penalizedCardinality(Matrix X)
Gets the value of a cardinality-penalized function.double
CovarianceSelectionProblem. penalizedL1(Matrix X)
Gets the value of an L1-penalized function.Constructors in dev.nm.stat.covariance.covarianceselection with parameters of type Matrix Constructor Description CovarianceSelectionProblem(Matrix S, double t)
Constructs a covariance selection problem. -
Uses of Matrix in dev.nm.stat.covariance.covarianceselection.lasso
Methods in dev.nm.stat.covariance.covarianceselection.lasso that return Matrix Modifier and Type Method Description Matrix
CovarianceSelectionGLASSOFAST. covariance()
Gets the estimated covariance matrix.Matrix
CovarianceSelectionLASSO. covariance()
Get the estimated covariance matrix.Matrix
CovarianceSelectionGLASSOFAST. inverseCovariance()
Gets the inverse of the estimated covariance matrix.Matrix
CovarianceSelectionLASSO. inverseCovariance()
Get the inverse of the estimated covariance matrix. -
Uses of Matrix in dev.nm.stat.covariance.nlshrink
Methods in dev.nm.stat.covariance.nlshrink that return Matrix Modifier and Type Method Description Matrix
LedoitWolf2016.Result. getShrunkCovarianceMatrix()
Gets the nonlinear shrinkage covariance matrix.Methods in dev.nm.stat.covariance.nlshrink with parameters of type Matrix Modifier and Type Method Description LedoitWolf2016.Result
LedoitWolf2016. estimate(Matrix Y)
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Uses of Matrix in dev.nm.stat.covariance.nlshrink.quest
Fields in dev.nm.stat.covariance.nlshrink.quest declared as Matrix Modifier and Type Field Description Matrix
QuEST.Result. lambda_Jacobian
lambda Jacobian -
Uses of Matrix in dev.nm.stat.descriptive.correlation
Classes in dev.nm.stat.descriptive.correlation that implement Matrix Modifier and Type Class Description class
CorrelationMatrix
The correlation matrix of n random variables X1, ..., Xn is the n × n matrix whose i,j entry is corr(Xi, Xj), the correlation between X1 and Xn.Constructors in dev.nm.stat.descriptive.correlation with parameters of type Matrix Constructor Description CorrelationMatrix(Matrix cov)
Construct a correlation matrix from a covariance matrix. -
Uses of Matrix in dev.nm.stat.descriptive.covariance
Classes in dev.nm.stat.descriptive.covariance that implement Matrix Modifier and Type Class Description class
SampleCovariance
This class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix.Constructors in dev.nm.stat.descriptive.covariance with parameters of type Matrix Constructor Description SampleCovariance(Matrix A)
Construct the covariance matrix of a matrix.SampleCovariance(Matrix A, boolean unbiased)
Construct the covariance matrix of a matrix. -
Uses of Matrix in dev.nm.stat.distribution.multivariate
Methods in dev.nm.stat.distribution.multivariate that return Matrix Modifier and Type Method Description Matrix
DirichletDistribution. covariance()
Matrix
MultinomialDistribution. covariance()
Matrix
MultivariateNormalDistribution. covariance()
Matrix
MultivariateProbabilityDistribution. covariance()
Gets the covariance matrix of this distribution.Matrix
MultivariateTDistribution. covariance()
Constructors in dev.nm.stat.distribution.multivariate with parameters of type Matrix 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 Matrix in dev.nm.stat.dlm.multivariate
Methods in dev.nm.stat.dlm.multivariate that return Matrix Modifier and Type Method Description Matrix
MultivariateStateEquation. G(int t)
Gets G(t), the coefficient matrix of xt - 1.Matrix
MultivariateStateEquation. H(int t)
Gets H(t), the covariance matrix of ut.Matrix
MultivariateStateEquation. W(int t)
Gets W(t), the covariance matrix of wt.Methods in dev.nm.stat.dlm.multivariate with parameters of type Matrix Modifier and Type Method Description ImmutableMatrix
MultivariateStateEquation. xt_var(int t, Matrix var_tlag_tlag)
Gets the variance of the apriori prediction for the next state.ImmutableMatrix
MultivariateObservationEquation. yt_var(int t, Matrix var_t_tlag)
Gets the covariance of the apriori prediction for the next observation.Constructors in dev.nm.stat.dlm.multivariate with parameters of type Matrix Constructor Description MultivariateDLM(Vector m0, Matrix C0, MultivariateObservationEquation Yt, MultivariateStateEquation Xt)
Construct a (multivariate) controlled dynamic linear model.MultivariateObservationEquation(Matrix F, Matrix V)
Constructs a time-invariant an observation equation.MultivariateObservationEquation(Matrix F, Matrix V, NormalRVG rmvnorm)
Constructs a time-invariant an observation equation.MultivariateStateEquation(Matrix G, Matrix W)
Constructs a time-invariant state equation without control variables.MultivariateStateEquation(Matrix G, Matrix H, Matrix W, NormalRVG rmvnorm)
Constructs a time-invariant state equation. -
Uses of Matrix in dev.nm.stat.evt.evd.bivariate
Methods in dev.nm.stat.evt.evd.bivariate that return Matrix Modifier and Type Method Description Matrix
AbstractBivariateEVD. covariance()
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Uses of Matrix in dev.nm.stat.evt.evd.univariate.fitting
Methods in dev.nm.stat.evt.evd.univariate.fitting that return Matrix Modifier and Type Method Description Matrix
EstimateByLogLikelihood. covarianceMatrix()
Get the covariance matrix, which is estimated as the inverse of negative Hessian matrix of the log-likelihood function valued at the fitted parameter. -
Uses of Matrix in dev.nm.stat.factor.factoranalysis
Constructors in dev.nm.stat.factor.factoranalysis with parameters of type Matrix Constructor Description FactorAnalysis(Matrix data, int nFactors)
Performs factor analysis on the data set, using Bartlett's weighted least-squares scores, and sample correlation matrix.FactorAnalysis(Matrix data, int nFactors, FactorAnalysis.ScoringRule rule)
Performs factor analysis on the data set with a user defined scoring rule.FactorAnalysis(Matrix data, int nFactors, FactorAnalysis.ScoringRule rule, Matrix S)
Performs factor analysis on the data set with a user defined scoring rule and a user defined covariance (or correlation) matrix. -
Uses of Matrix in dev.nm.stat.factor.implicitmodelpca
Methods in dev.nm.stat.factor.implicitmodelpca that return Matrix Modifier and Type Method Description Matrix
ExplicitImplicitModelPCA.Result. B()
Gets B, the implicit factor loading matrix.Matrix
ImplicitModelPCA.Result. B()
Gets B, the factor loading matrix.Matrix
ExplicitImplicitModelPCA.Result. E()
Gets E, the residual matrix.Matrix
ImplicitModelPCA.Result. E()
Gets E, the residual matrix.Matrix
ExplicitImplicitModelPCA.Result. F()
Gets F, the implicit factor value matrix.Matrix
ImplicitModelPCA.Result. F()
Gets F, the factor value matrix.Matrix
ExplicitImplicitModelPCA.Result. Gamma()
Gets Γ, the explicit factor loading matrix.Constructors in dev.nm.stat.factor.implicitmodelpca with parameters of type Matrix Constructor Description AverageImplicitModelPCA(Matrix R, double varExplained)
Constructs an explicit-implicit model for a time series of vectored observationsAverageImplicitModelPCA(Matrix R, int K)
Constructs an explicit-implicit model for a time series of vectored observationsExplicitImplicitModelPCA(Matrix R, Matrix G, double varExplained)
ExplicitImplicitModelPCA(Matrix R, Matrix G, int K)
ImplicitModelPCA(Matrix R)
Constructs an implicit-model that will have one and only one implicit factors.ImplicitModelPCA(Matrix R, double varExplained)
Constructs an implicit-model that will have the number of implicit factors such that the variance explained is bigger than a thresholdImplicitModelPCA(Matrix R, int K)
Constructs an implicit-model that will have K implicit factors. -
Uses of Matrix in dev.nm.stat.factor.pca
Methods in dev.nm.stat.factor.pca that return Matrix Modifier and Type Method Description Matrix
PCA. loadings()
Gets the matrix of variable loadings.Matrix
PCAbyEigen. loadings()
Matrix
PCAbySVD. loadings()
Matrix
PCA. scores()
Gets the scores of supplied data on the principal components.Matrix
PCAbyEigen. V()
Gets the correlation (or covariance) matrix used by the PCA.Matrix
PCA. X()
Gets the (possibly centered and/or scaled) data matrix X used for the PCA.Constructors in dev.nm.stat.factor.pca with parameters of type Matrix Constructor Description PCAbyEigen(Matrix data)
Performs Principal Component Analysis, using the eigen method and using correlation matrix, on a data matrix.PCAbyEigen(Matrix data, boolean correlation)
Performs Principal Component Analysis, using the eigen method, on a data matrix.PCAbyEigen(Matrix data, boolean correlation, Matrix V)
Performs Principal Component Analysis, using the eigen method, on a data matrix with an optional correlation (or covariance) matrix provided.PCAbySVD(Matrix data)
Performs Principal Component Analysis, using the preferred SVD method, on a centered and scaled data matrix.PCAbySVD(Matrix data, boolean centered, boolean scaled)
Performs Principal Component Analysis, using the preferred SVD method, on a data matrix (possibly centered and/or scaled).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 Matrix in dev.nm.stat.hmm
Constructors in dev.nm.stat.hmm with parameters of type Matrix Constructor Description HiddenMarkovModel(Vector PI, Matrix A, RandomNumberGenerator[] B)
HMMRNG(Vector PI, Matrix A, RandomNumberGenerator[] B)
Constructs a hidden Markov model. -
Uses of Matrix in dev.nm.stat.hmm.discrete
Methods in dev.nm.stat.hmm.discrete that return Matrix Modifier and Type Method Description static Matrix[]
BaumWelch. xi(HiddenMarkovModel model, int[] observations, ForwardBackwardProcedure fb)
Gets the ξ matrices, where for 1 ≤ t ≤ T - 1, the t-th entry of ξ is an (N * N) matrix, for which the (i, j)-th entry is ξt(i, j).Methods in dev.nm.stat.hmm.discrete with parameters of type Matrix 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 Matrix Constructor Description DiscreteHMM(Vector PI, Matrix A, Matrix B)
Constructs a discrete hidden Markov model. -
Uses of Matrix in dev.nm.stat.hmm.mixture
Constructors in dev.nm.stat.hmm.mixture with parameters of type Matrix Constructor Description MixtureHMM(Vector PI, Matrix A, MixtureDistribution dist)
Constructs a mixture hidden Markov model. -
Uses of Matrix in dev.nm.stat.markovchain
Methods in dev.nm.stat.markovchain with parameters of type Matrix Modifier and Type Method Description 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 Matrix Constructor Description SimpleMC(Vector PI, Matrix A)
Constructs a time-homogeneous Markov chain with a finite state space. -
Uses of Matrix in dev.nm.stat.random.rng.multivariate
Constructors in dev.nm.stat.random.rng.multivariate with parameters of type Matrix 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 Matrix in dev.nm.stat.random.rng.multivariate.mcmc.metropolis
Methods in dev.nm.stat.random.rng.multivariate.mcmc.metropolis that return Matrix Modifier and Type Method Description Matrix
RobustAdaptiveMetropolis. S()
Gets the tuned scaling matrix (this changes each time a new sample is drawn).Constructors in dev.nm.stat.random.rng.multivariate.mcmc.metropolis with parameters of type Matrix Constructor Description 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.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 Matrix in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction
Constructors in dev.nm.stat.random.rng.multivariate.mcmc.proposalfunction with parameters of type Matrix Constructor Description GaussianProposalFunction(Matrix scale, RandomLongGenerator uniform)
Constructs a Gaussian proposal function. -
Uses of Matrix in dev.nm.stat.random.sampler.resampler.multivariate
Methods in dev.nm.stat.random.sampler.resampler.multivariate that return Matrix Modifier and Type Method Description Matrix
GroupResampler. newResample()
Matrix
MultivariateResampler. newResample()
Gets a resample from the original sample.Constructors in dev.nm.stat.random.sampler.resampler.multivariate with parameters of type Matrix Constructor Description GroupResampler(Matrix sample)
Constructs a re-sampler that treats each row as a group object, shuffling the groups/rows.GroupResampler(Matrix sample, Resampler resampler)
Constructs a re-sampler that treats each row as a group object, shuffling the groups/rows. -
Uses of Matrix in dev.nm.stat.regression.linear
Constructors in dev.nm.stat.regression.linear with parameters of type Matrix Constructor Description 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 Matrix in dev.nm.stat.regression.linear.glm
Constructors in dev.nm.stat.regression.linear.glm with parameters of type Matrix Constructor Description GLMProblem(Vector y, Matrix X, boolean intercept, GLMFamily family)
Construct a GLM problem. -
Uses of Matrix in dev.nm.stat.regression.linear.glm.modelselection
Methods in dev.nm.stat.regression.linear.glm.modelselection that return Matrix Modifier and Type Method Description protected Matrix
GLMModelSelection. subA()
Constructs a covariates subset.Methods in dev.nm.stat.regression.linear.glm.modelselection with parameters of type Matrix Modifier and Type Method Description int
BackwardElimination.Step. eliminate(GLMProblem problem, Matrix subA)
int
EliminationByAIC. eliminate(GLMProblem problem, Matrix subA)
int
EliminationByZValue. eliminate(GLMProblem problem, Matrix subA)
int
ForwardSelection.Step. select(GLMProblem problem, Matrix subA, int[] factorChoices)
int
SelectionByAIC. select(GLMProblem problem, Matrix subA, int[] factorChoices)
int
SelectionByZValue. select(GLMProblem problem, Matrix subA, int[] factorChoices)
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Uses of Matrix in dev.nm.stat.regression.linear.glm.quasi
Constructors in dev.nm.stat.regression.linear.glm.quasi with parameters of type Matrix Constructor Description QuasiGLMProblem(Vector y, Matrix X, boolean intercept, QuasiFamily quasiFamily)
Constructs a quasi GLM problem. -
Uses of Matrix in dev.nm.stat.regression.linear.lasso
Constructors in dev.nm.stat.regression.linear.lasso with parameters of type Matrix 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 Matrix in dev.nm.stat.regression.linear.lasso.lars
Methods in dev.nm.stat.regression.linear.lasso.lars that return Matrix Modifier and Type Method Description Matrix
LARSFitting.Estimators. scaledBetas()
Gets the entire sequence of estimated (LARS) regression coefficients, scaled by the L2 norm of each row.Matrix
LARSProblem. XLARS()
Gets the matrix of covariates (possibly demeaned and/or scaled) to be used in LARS.Constructors in dev.nm.stat.regression.linear.lasso.lars with parameters of type Matrix 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 Matrix in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients
Methods in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients that return Matrix Modifier and Type Method Description Matrix
ConstantSigma1. evaluate(MultivariateFt ft)
Matrix
DiffusionMatrix. evaluate(MultivariateFt ft)
Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.Matrix
DiffusionSigma. evaluate(MultivariateFt ft)
Constructors in dev.nm.stat.stochasticprocess.multivariate.sde.coefficients with parameters of type Matrix Constructor Description ConstantSigma1(Matrix sigma)
Construct a constant diffusion coefficient function.ConstantSigma2(Matrix sigma)
Deprecated.Construct a constant diffusion coefficient function. -
Uses of Matrix in dev.nm.stat.stochasticprocess.multivariate.sde.discrete
Constructors in dev.nm.stat.stochasticprocess.multivariate.sde.discrete with parameters of type Matrix Constructor Description MultivariateBrownianSDE(Vector mu, Matrix sigma)
Construct a multi-dimensional Brownian motion. -
Uses of Matrix in dev.nm.stat.test.distribution.pearson
Methods in dev.nm.stat.test.distribution.pearson that return Matrix Modifier and Type Method Description static Matrix
ChiSquareIndependenceTest. getExpectedContingencyTable(int[] rowSums, int[] colSums)
Assume the null hypothesis of independence, we compute the expected frequency of each category.Methods in dev.nm.stat.test.distribution.pearson with parameters of type Matrix Modifier and Type Method Description boolean
AS159. isValidated(Matrix A)
Checks whether a matrix satisfies the row and column sums.static double
ChiSquareIndependenceTest. pearsonStat(Matrix O, Matrix E, boolean YatesContinuityCorrection)
Compute the Pearson's cumulative test statistic, which asymptotically approaches a χ2 distribution.Constructors in dev.nm.stat.test.distribution.pearson with parameters of type Matrix Constructor Description ChiSquareIndependenceTest(Matrix sample)
Assess whether the two random variables in the contingency table are independent.ChiSquareIndependenceTest(Matrix sample, int nSims, ChiSquareIndependenceTest.Type type)
Assess whether the two random variables in the contingency table are independent. -
Uses of Matrix in dev.nm.stat.timeseries.datastructure.multivariate
Methods in dev.nm.stat.timeseries.datastructure.multivariate that return Matrix Modifier and Type Method Description Matrix
MultivariateGenericTimeTimeSeries. toMatrix()
Matrix
MultivariateTimeSeries. toMatrix()
Convert this multivariate time series into an m x n matrix, where m is the dimension, and n the length.Constructors in dev.nm.stat.timeseries.datastructure.multivariate with parameters of type Matrix Constructor Description MultivariateGenericTimeTimeSeries(T[] timestamps, Matrix values)
Construct a multivariate time series from timestamps and vectors. -
Uses of Matrix in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime
Methods in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime that return Matrix Modifier and Type Method Description Matrix
MultivariateSimpleTimeSeries. toMatrix()
Constructors in dev.nm.stat.timeseries.datastructure.multivariate.realtime.inttime with parameters of type Matrix Constructor Description MultivariateSimpleTimeSeries(Matrix values)
Construct an instance ofMultivariateSimpleTimeSeries
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Uses of Matrix in dev.nm.stat.timeseries.datastructure.univariate
Methods in dev.nm.stat.timeseries.datastructure.univariate that return Matrix Modifier and Type Method Description static Matrix
UnivariateTimeSeriesUtils. toMatrix(UnivariateTimeSeries<?,?> ts)
Cast a time series into a column matrix, discarding the timestamps. -
Uses of Matrix in dev.nm.stat.timeseries.linear.multivariate
Methods in dev.nm.stat.timeseries.linear.multivariate that return Matrix Modifier and Type Method Description Matrix
MultivariateAutoCorrelationFunction. get(int i, int j)
Get the auto-correlation of Xi and Xj.Matrix
MultivariateAutoCovarianceFunction. get(int i, int j)
Get the auto-covariance matrix for Xi and Xj. -
Uses of Matrix in dev.nm.stat.timeseries.linear.multivariate.arima
Constructors in dev.nm.stat.timeseries.linear.multivariate.arima with parameters of type Matrix Constructor Description VARIMAModel(Matrix[] phi, int d, Matrix[] theta)
Construct a multivariate ARIMA model with unit variance and zero-intercept (mu).VARIMAModel(Matrix[] phi, int d, Matrix[] theta, Matrix sigma)
Construct a multivariate ARIMA model with zero-intercept (mu).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.VARIMAXModel(Matrix[] phi, int d, Matrix[] theta, Matrix psi)
Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).VARIMAXModel(Matrix[] phi, int d, Matrix[] theta, Matrix psi, Matrix sigma)
Construct a multivariate ARIMAX model with zero-intercept (mu).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 Matrix in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma
Methods in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma that return Matrix Modifier and Type Method Description Matrix
VARMAXModel. armaxMean(Matrix arLags, Matrix maLags, Vector exVar)
Compute the multivariate ARMAX conditional mean.Matrix
VARMAAutoCorrelation. evaluate(double i)
Get the i-th auto-correlation matrix.Matrix
VARMAAutoCorrelation. evaluate(double i, double j)
Matrix
VARMAAutoCovariance. evaluate(double i)
Get the i-th auto-covariance matrix.Matrix
VARMAAutoCovariance. evaluate(double i, double j)
Matrix[]
VMAInvertibility. PI()
Get the coefficients of the linear representation of the time series.Methods in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma with parameters of type Matrix Modifier and Type Method Description Matrix
VARMAXModel. armaxMean(Matrix arLags, Matrix maLags, Vector exVar)
Compute the multivariate ARMAX conditional mean.Vector
VARMAModel. conditionalMean(Matrix arLags, Matrix maLags)
Compute the multivariate ARMA conditional mean, given all the lags.Constructors in dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma with parameters of type Matrix Constructor Description VARMAModel(Matrix[] phi, Matrix[] theta)
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).VARMAModel(Matrix[] phi, Matrix[] theta, Matrix sigma)
Construct a multivariate ARMA model with zero-intercept (mu).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(Matrix[] phi, Matrix[] theta, Matrix psi)
Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).VARMAXModel(Matrix[] phi, Matrix[] theta, Matrix psi, Matrix sigma)
Construct a multivariate ARMAX model with zero-intercept (mu).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(Matrix[] phi)
Construct a VAR model with unit variance and zero-intercept (mu).VARModel(Matrix[] phi, Matrix sigma)
Construct a VAR model with zero-intercept (mu).VARModel(Vector mu, Matrix[] phi)
Construct a VAR model with unit variance.VARModel(Vector mu, Matrix[] phi, Matrix sigma)
Construct a VAR model.VARXModel(Matrix[] phi, Matrix psi)
Construct a VARX model with unit variance and zero-mean.VARXModel(Matrix[] phi, Matrix psi, Matrix sigma)
Construct a VARX model with zero-mean.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(Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a long-run VECM(p) model with zero-intercept (mu).VECMLongrun(Vector mu, Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a long-run VECM(p) model.VECMTransitory(Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a transitory VECM(p) model with zero-intercept (mu).VECMTransitory(Vector mu, Matrix pi, Matrix[] gamma, Matrix psi, Matrix sigma)
Construct a transitory VECM(p) model.VMAModel(Matrix[] theta)
Construct a multivariate MA model with unit variance and zero-mean.VMAModel(Matrix[] theta, Matrix sigma)
Construct a multivariate MA model with zero-mean.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 Matrix in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Methods in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that return Matrix Modifier and Type Method Description Matrix
ARMAFit. covariance()
Get the asymptotic covariance matrix of the estimators.Matrix
ConditionalSumOfSquares. covariance()
Get the asymptotic covariance matrix of the estimated parameters, φ and θ. -
Uses of Matrix in tech.nmfin.meanreversion.cointegration
Fields in tech.nmfin.meanreversion.cointegration declared as Matrix Modifier and Type Field Description protected Matrix
TradingPair. returns
Methods in tech.nmfin.meanreversion.cointegration that return Matrix Modifier and Type Method Description protected static Matrix
RobustCointegration. filterPrices(Matrix prices0)
Filters out invalid prices.protected static Matrix
RobustCointegration. filterUnchangedPrices(Matrix prices0)
Filters out prices that are unchanged between consecutive times.static Matrix
PairingModelUtils. getPriceMatrix(Vector p1, Vector p2)
static Matrix
PairingModelUtils. getTailedMatrix(Matrix A, double tailed)
protected static Matrix
RobustCointegration. getTailedMatrix(Matrix A, double tailed)
Methods in tech.nmfin.meanreversion.cointegration with parameters of type Matrix Modifier and Type Method Description protected static double
RobustCointegration. computeCointegratingBeta(Matrix prices)
protected static double
RobustCointegration. computeShortTermCointegratingBeta(Matrix prices, double tailed)
protected static Matrix
RobustCointegration. filterPrices(Matrix prices0)
Filters out invalid prices.protected static Matrix
RobustCointegration. filterUnchangedPrices(Matrix prices0)
Filters out prices that are unchanged between consecutive times.static List<List<Integer>>
PairingModelUtils. findClusters(Matrix R, double covPenalty)
static Map<Integer,Integer>
PairingModelUtils. getClusters(Matrix returns, double covPenalty)
static Matrix
PairingModelUtils. getTailedMatrix(Matrix A, double tailed)
protected static Matrix
RobustCointegration. getTailedMatrix(Matrix A, double tailed)
List<TradingPair>
PairingModel. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
List<TradingPair>
PairingModel1. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
List<TradingPair>
PairingModel2. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
List<TradingPair>
PairingModel3. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
List<TradingPair>
PairingModel4. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
List<TradingPair>
PairingModel5. getTradingPairs(List<String> allSymbols, List<String> tradableSymbols, Matrix prices)
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Uses of Matrix in tech.nmfin.meanreversion.cointegration.check
Methods in tech.nmfin.meanreversion.cointegration.check with parameters of type Matrix Modifier and Type Method Description static double
CorrelationCheck. cor(Matrix A, double tailed)
Constructors in tech.nmfin.meanreversion.cointegration.check with parameters of type Matrix Constructor Description CorrelationCheck(Matrix A, double lowerBound, double upperBound)
CorrelationCheck(Matrix A, double tail, double lowerBound, double upperBound)
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Uses of Matrix in tech.nmfin.meanreversion.daspremont2008
Methods in tech.nmfin.meanreversion.daspremont2008 that return Matrix Modifier and Type Method Description Matrix
AhatEstimation. compute()
Constructors in tech.nmfin.meanreversion.daspremont2008 with parameters of type Matrix Constructor Description AhatEstimation(Matrix St, Matrix St_m1, double gamma)
Estimates the coefficient matrix of a vector autoregressive process of order 1.CovarianceEstimation(Matrix Sigma, double rho)
Solves the maximum likelihood problem for covariance selection.ExtremalGeneralizedEigenvalueByGreedySearch(Matrix A, Matrix B)
Constructs the problem described in Section 3.2, d'Aspremont (2008), changed to a minimization problem.ExtremalGeneralizedEigenvalueByGreedySearch(Matrix A, Matrix B, boolean isMinimizationProblem)
Constructs the problem described in Section 3.2, d'Aspremont (2008).IndependentCoVAR(Matrix cov, double tol)
Runs the algorithm with the given covariance matrix. -
Uses of Matrix in tech.nmfin.portfoliooptimization
Methods in tech.nmfin.portfoliooptimization that return Matrix Modifier and Type Method Description default Matrix
PortfolioOptimizationAlgorithm.CovarianceEstimator. getCovariances(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Matrix
PortfolioOptimizationAlgorithm.SampleCovarianceEstimator. getCovariances(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Methods in tech.nmfin.portfoliooptimization with parameters of type Matrix Modifier and Type Method Description default Matrix
PortfolioOptimizationAlgorithm.CovarianceEstimator. getCovariances(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
Matrix
PortfolioOptimizationAlgorithm.SampleCovarianceEstimator. getCovariances(Matrix returns, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
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 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. -
Uses of Matrix in tech.nmfin.portfoliooptimization.clm
Constructors in tech.nmfin.portfoliooptimization.clm with parameters of type Matrix 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 Matrix in tech.nmfin.portfoliooptimization.corvalan2005
Methods in tech.nmfin.portfoliooptimization.corvalan2005 with parameters of type Matrix 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. -
Uses of Matrix in tech.nmfin.portfoliooptimization.lai2010
Methods in tech.nmfin.portfoliooptimization.lai2010 with parameters of type Matrix Modifier and Type Method Description Lai2010NPEBModel.OptimalWeights
Lai2010NPEBModel. optimalWeights(Matrix returns, double lambda)
Computes the weights based on given historical returns and the risk-aversion index λ. -
Uses of Matrix in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb
Constructors in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb with parameters of type Matrix Constructor Description NPEBPortfolioMomentsEstimator(Matrix returns, ReturnsMoments.Estimator returnMomentsEstimator, MVOptimizer mvOptimizer, ReturnsResamplerFactory resamplerFactory, int nBootstrapSamples)
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Uses of Matrix in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler
Fields in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler declared as Matrix Modifier and Type Field Description Matrix
GARCHResamplerFactory2. sigma2
Matrix
GARCHResamplerFactory2. stdinno
Methods in tech.nmfin.portfoliooptimization.lai2010.ceta.npeb.resampler with parameters of type Matrix Modifier and Type Method Description protected ResamplerModel
ARResamplerFactory. getResamplerModel(Matrix returns)
protected ResamplerModel
GARCHResamplerFactory. getResamplerModel(Matrix returns)
protected abstract ResamplerModel
ModelResamplerFactory. getResamplerModel(Matrix returns)
MultivariateResampler
GARCHResamplerFactory2. newResampler(Matrix returns)
MultivariateResampler
GroupResamplerFactory. newResampler(Matrix returns)
MultivariateResampler
ModelResamplerFactory. newResampler(Matrix returns)
MultivariateResampler
ReturnsResamplerFactory. newResampler(Matrix returns)
Constructs a new instance of a re-sampling mechanism. -
Uses of Matrix in tech.nmfin.portfoliooptimization.lai2010.fit
Methods in tech.nmfin.portfoliooptimization.lai2010.fit that return Matrix Modifier and Type Method Description Matrix
ResamplerModel. fittedValues()
static Matrix
ResamplerModel. fittedValues(OLSResiduals[] residuals)
Matrix
SimpleAR1Fit. fittedValues()
Matrix
SimpleGARCHFit. fittedValues()
Matrix
ResamplerModel. sigma2()
Gets the conditional variances of residuals over time.Matrix
SimpleAR1Fit. sigma2()
Matrix
SimpleGARCHFit. sigma2()
Matrix
ResamplerModel. standarizedInnovations()
Gets the standarized innovations (normalized by the conditional standard deviation at the time) of the time series.Matrix
SimpleAR1Fit. standarizedInnovations()
Matrix
SimpleGARCHFit. standarizedInnovations()
Methods in tech.nmfin.portfoliooptimization.lai2010.fit with parameters of type Matrix Modifier and Type Method Description ReturnsMoments
SimpleAR1Moments. estimate(Matrix returns)
ReturnsMoments
SimpleGARCHMoments1. estimate(Matrix returns)
ReturnsMoments
SimpleGARCHMoments2. estimate(Matrix stdinno)
Constructors in tech.nmfin.portfoliooptimization.lai2010.fit with parameters of type Matrix Constructor Description SimpleAR1Fit(Matrix mts)
SimpleGARCHFit(Matrix mts)
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Uses of Matrix in tech.nmfin.portfoliooptimization.lai2010.optimizer
Methods in tech.nmfin.portfoliooptimization.lai2010.optimizer with parameters of type Matrix 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)
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Uses of Matrix in tech.nmfin.portfoliooptimization.markowitz
Constructors in tech.nmfin.portfoliooptimization.markowitz with parameters of type Matrix 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 Matrix in tech.nmfin.portfoliooptimization.nmsaam
Methods in tech.nmfin.portfoliooptimization.nmsaam with parameters of type Matrix Modifier and Type Method Description Vector
NMSAAM. getOptimalWeights(Matrix returns, Vector weights0, PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup, LocalDateTimeInterval interval)
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Uses of Matrix in tech.nmfin.portfoliooptimization.socp.constraints
Constructors in tech.nmfin.portfoliooptimization.socp.constraints with parameters of type Matrix Constructor Description 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. -
Uses of Matrix in tech.nmfin.portfoliooptimization.socp.constraints.ybar
Constructors in tech.nmfin.portfoliooptimization.socp.constraints.ybar with parameters of type Matrix Constructor Description 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. -
Uses of Matrix in tech.nmfin.returns
Classes in tech.nmfin.returns that implement Matrix Modifier and Type Class Description class
ReturnsMatrix
Methods in tech.nmfin.returns with parameters of type Matrix Modifier and Type Method Description static Vector
Returns. getMeanReturns(Matrix returns)
Computes a vector of mean returns of the input returns (one column for one asset).Constructors in tech.nmfin.returns with parameters of type Matrix Constructor Description ReturnsMatrix(Matrix prices)
ReturnsMatrix(Matrix prices, ReturnsCalculator calc)
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Uses of Matrix in tech.nmfin.returns.moments
Methods in tech.nmfin.returns.moments that return Matrix Modifier and Type Method Description Matrix
ReturnsMoments. V()
Gets the second moment matrix.Methods in tech.nmfin.returns.moments with parameters of type Matrix Modifier and Type Method Description ReturnsMoments
MomentsEstimatorLedoitWolf. estimate(Matrix returns)
ReturnsMoments
ReturnsMoments.Estimator. estimate(Matrix returns)
Estimates the moments from a given returns matrix.Constructors in tech.nmfin.returns.moments with parameters of type Matrix Constructor Description ReturnsMoments(Vector mu, Matrix V)
Constructs an instance. -
Uses of Matrix in tech.nmfin.signal.infantino2010
Methods in tech.nmfin.signal.infantino2010 that return Matrix Modifier and Type Method Description Matrix
Infantino2010PCA.Signal. B()
Matrix
Infantino2010PCA.Signal. D()
Matrix
Infantino2010PCA.Signal. X()
Methods in tech.nmfin.signal.infantino2010 with parameters of type Matrix Modifier and Type Method Description Infantino2010Regime.Regime
Infantino2010Regime. getRegime(Matrix D)
Gets the current regime.Infantino2010PCA.Signal
Infantino2010PCA. getSignal(Matrix X)
static Vector
Infantino2010PCA. sumUpLastRows(Matrix D, int lastIndex, int nRows)
Sums up, for each column, the lastnRows
rows.
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