Modifier and Type | Class and Description |
---|---|
class |
ImmutableMatrix
This is a read-only view of a
Matrix instance. |
Modifier and Type | Method and Description |
---|---|
Matrix |
MatrixRing.add(Matrix that)
this + that
|
Matrix |
ImmutableMatrix.add(Matrix that) |
Matrix |
ImmutableMatrix.deepCopy()
Make a deep copy of the underlying matrix.
|
Matrix |
Matrix.deepCopy() |
Matrix |
MatrixRing.minus(Matrix that)
this - that
|
Matrix |
ImmutableMatrix.minus(Matrix that) |
Matrix |
MatrixRing.multiply(Matrix that)
this * that
|
Matrix |
ImmutableMatrix.multiply(Matrix that) |
Matrix |
MatrixRing.ONE()
Get an identity matrix that has the same dimension as this matrix.
|
Matrix |
ImmutableMatrix.ONE() |
Matrix |
MatrixRing.opposite()
Get the opposite of this matrix.
|
Matrix |
ImmutableMatrix.opposite() |
Matrix |
ImmutableMatrix.scaled(double c) |
Matrix |
Matrix.scaled(double c)
Scale this matrix, A, by a constant.
|
Matrix |
MatrixRing.t()
Get the transpose of this matrix.
|
Matrix |
ImmutableMatrix.t() |
Matrix |
MatrixRing.ZERO()
Get a zero matrix that has the same dimension as this matrix.
|
Matrix |
ImmutableMatrix.ZERO() |
Modifier and Type | Method and Description |
---|---|
Matrix |
MatrixRing.add(Matrix that)
this + that
|
Matrix |
ImmutableMatrix.add(Matrix that) |
static 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 |
MatrixRing.minus(Matrix that)
this - that
|
Matrix |
ImmutableMatrix.minus(Matrix that) |
Matrix |
MatrixRing.multiply(Matrix that)
this * that
|
Matrix |
ImmutableMatrix.multiply(Matrix that) |
Constructor and Description |
---|
ImmutableMatrix(Matrix A)
Construct a read-only version of a matrix.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
TriDiagonalization.Q()
Gets Q, such that Q * A * Q = T.
|
Matrix |
SymmetricTridiagonalDecomposition.Q()
Returns the rotation matrix.
|
Matrix |
BiDiagonalizationByGolubKahanLanczos.U() |
Matrix |
BiDiagonalizationByHouseholder.U()
Gets U, where
U' = Uk * ...
|
Matrix |
BiDiagonalization.U()
Gets U, where
U' = Uk * ...
|
Matrix |
BiDiagonalizationByGolubKahanLanczos.V() |
Matrix |
BiDiagonalizationByHouseholder.V()
Gets V, where
V' = Vk * ...
|
Matrix |
BiDiagonalization.V()
Gets V, where
V' = Vk * ...
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
EigenDecomposition.Q()
Get Q as in Q * D * Q' = A.
|
Matrix |
EigenDecomposition.Qt()
Get Q' as in Q * D * Q' = A.
|
Constructor and 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)
Use
Eigen.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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SymmetricEigenByMR3.getEigenvectorMatrix() |
Matrix |
MR3.getEigenvectorMatrix()
Gets the eigenvector matrix, each column is an eigenvector.
|
Constructor and 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.
|
Modifier and Type | Method and 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 |
QRAlgorithm.Q()
Gets the Q matrix as in the real Schur canonical form Q'AQ = T.
|
Matrix |
HessenbergDecomposition.Q()
Gets the Q matrix, where
\[
Q = (Q_1 \times ...
|
Matrix |
SymmetricQRAlgorithm.Q()
Gets the Q matrix as in Q'AQ = D, where D is diagonal and Q is
orthogonal.
|
Matrix |
QRAlgorithm.T() |
Modifier and Type | Method and Description |
---|---|
List<Matrix> |
QRAlgorithm.Qs()
Gets the list of Qi's produced in the process of the QR
algorithm (if
keepQs is set to true ). |
List<Matrix> |
Hessenberg.reduce(Matrix H)
Deprecated.
Not supported yet.
|
Modifier and Type | Method and Description |
---|---|
Deflation |
TridiagonalDeflationSearch.backSearch(Matrix D) |
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,
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 if
H[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.
|
Constructor and 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.
|
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
HouseholderQR.Q()
Gets the Q matrix in the QR decomposition.
|
Matrix |
QRDecomposition.Q()
Get the orthogonal Q matrix in the QR decomposition, A = QR.
|
Matrix |
GramSchmidt.Q() |
Matrix |
QR.Q() |
Matrix |
HouseholderQR.squareQ() |
Matrix |
QRDecomposition.squareQ()
Get the square Q matrix.
|
Matrix |
GramSchmidt.squareQ()
Get the square Q matrix.
|
Matrix |
QR.squareQ() |
Matrix |
HouseholderQR.tallR() |
Matrix |
QRDecomposition.tallR()
Get the tall R matrix.
|
Matrix |
GramSchmidt.tallR() |
Matrix |
QR.tallR() |
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SVDDecomposition.U()
Get the U matrix as in SVD decomposition.
|
Matrix |
GolubKahanSVD.U() |
Matrix |
SVD.U() |
Matrix |
SVDDecomposition.Ut()
Get the transpose of U, i.e.,
U().t() . |
Matrix |
GolubKahanSVD.Ut() |
Matrix |
SVD.Ut() |
Matrix |
SVDDecomposition.V()
Get the V matrix as in SVD decomposition.
|
Matrix |
GolubKahanSVD.V() |
Matrix |
SVD.V() |
Constructor and 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.
|
Modifier and Type | Method and 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() |
Constructor and Description |
---|
SVDbyMR3(Matrix A,
boolean doUV)
Creates a singular value decomposition for a matrix A.
|
Constructor and 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.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
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 |
BackwardSubstitution.solve(UpperTriangularMatrix U,
Matrix B) |
Matrix |
BackwardSubstitution.solve(UpperTriangularMatrix U,
Matrix B,
double epsilon) |
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
Matrix |
BackwardSubstitution.solve(UpperTriangularMatrix U,
Matrix B) |
Matrix |
BackwardSubstitution.solve(UpperTriangularMatrix U,
Matrix B,
double epsilon) |
Constructor and 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.
|
Modifier and Type | Class and 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.
|
Modifier and Type | Method and 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 GivensMatrix
|
Matrix |
PermutationMatrix.ZERO() |
Modifier and Type | Method and 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.
|
Modifier and Type | Class and Description |
---|---|
class |
DenseMatrix
This class implements the standard, dense,
double based matrix
representation. |
Modifier and Type | Method and Description |
---|---|
Matrix |
DenseMatrix.add(Matrix that) |
Matrix |
DenseMatrix.minus(Matrix that) |
Matrix |
DenseMatrix.multiply(Matrix that) |
Matrix |
DenseMatrix.opposite() |
Modifier and Type | Method and Description |
---|---|
Matrix |
DenseMatrix.add(Matrix that) |
Matrix |
DenseMatrix.minus(Matrix that) |
Matrix |
DenseMatrix.multiply(Matrix that) |
Constructor and Description |
---|
DenseMatrix(Matrix A)
Converts any matrix to the standard matrix representation.
|
Modifier and Type | Class and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
DiagonalMatrix.add(Matrix that)
Computes the sum of two diagonal matrices.
|
Matrix |
BidiagonalMatrix.add(Matrix that) |
Matrix |
TridiagonalMatrix.add(Matrix that) |
Matrix |
DiagonalMatrix.minus(Matrix that)
Computes the difference between two diagonal matrices.
|
Matrix |
BidiagonalMatrix.minus(Matrix that) |
Matrix |
TridiagonalMatrix.minus(Matrix that) |
Matrix |
DiagonalMatrix.multiply(Matrix that)
Computes the product of two diagonal matrices.
|
Matrix |
BidiagonalMatrix.multiply(Matrix that)
this * that
|
Modifier and Type | Method and Description |
---|---|
Matrix |
DiagonalMatrix.add(Matrix that)
Computes the sum of two diagonal matrices.
|
Matrix |
BidiagonalMatrix.add(Matrix that) |
Matrix |
TridiagonalMatrix.add(Matrix that) |
Matrix |
DiagonalMatrix.minus(Matrix that)
Computes the difference between two diagonal matrices.
|
Matrix |
BidiagonalMatrix.minus(Matrix that) |
Matrix |
TridiagonalMatrix.minus(Matrix that) |
Matrix |
DiagonalMatrix.multiply(Matrix that)
Computes the product of two diagonal matrices.
|
Matrix |
BidiagonalMatrix.multiply(Matrix that)
this * that
|
Constructor and Description |
---|
TridiagonalMatrix(Matrix A)
Casts a matrix to tridiagonal by copying the 3 diagonals (ignoring all other entries).
|
Modifier and Type | Class and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
UpperTriangularMatrix.add(Matrix that) |
Matrix |
LowerTriangularMatrix.add(Matrix that) |
Matrix |
SymmetricMatrix.add(Matrix that) |
Matrix |
UpperTriangularMatrix.minus(Matrix that) |
Matrix |
LowerTriangularMatrix.minus(Matrix that) |
Matrix |
SymmetricMatrix.minus(Matrix that) |
Matrix |
UpperTriangularMatrix.multiply(Matrix that) |
Matrix |
LowerTriangularMatrix.multiply(Matrix that) |
Matrix |
SymmetricMatrix.multiply(Matrix that) |
Modifier and Type | Method and Description |
---|---|
Matrix |
UpperTriangularMatrix.add(Matrix that) |
Matrix |
LowerTriangularMatrix.add(Matrix that) |
Matrix |
SymmetricMatrix.add(Matrix that) |
Matrix |
UpperTriangularMatrix.minus(Matrix that) |
Matrix |
LowerTriangularMatrix.minus(Matrix that) |
Matrix |
SymmetricMatrix.minus(Matrix that) |
Matrix |
UpperTriangularMatrix.multiply(Matrix that) |
Matrix |
LowerTriangularMatrix.multiply(Matrix that) |
Matrix |
SymmetricMatrix.multiply(Matrix that) |
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SimpleMatrixMathOperation.add(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
MatrixMathOperation.add(MatrixAccess A1,
MatrixAccess A2)
A1 + A2
|
Matrix |
AutoParallelMatrixMathOperation.add(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
SimpleMatrixMathOperation.minus(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
MatrixMathOperation.minus(MatrixAccess A1,
MatrixAccess A2)
A1 - A2
|
Matrix |
AutoParallelMatrixMathOperation.minus(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
SimpleMatrixMathOperation.multiply(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
MatrixMathOperation.multiply(MatrixAccess A1,
MatrixAccess A2)
A1 * A2
|
Matrix |
AutoParallelMatrixMathOperation.multiply(MatrixAccess A1,
MatrixAccess A2) |
Matrix |
SimpleMatrixMathOperation.scaled(MatrixAccess A,
double scalar) |
Matrix |
MatrixMathOperation.scaled(MatrixAccess A,
double c)
c * A
|
Matrix |
AutoParallelMatrixMathOperation.scaled(MatrixAccess A,
double c) |
Matrix |
SimpleMatrixMathOperation.transpose(MatrixAccess A) |
Matrix |
MatrixMathOperation.transpose(MatrixAccess A)
Get the transpose of A.
|
Matrix |
AutoParallelMatrixMathOperation.transpose(MatrixAccess A) |
Modifier and Type | Interface and Description |
---|---|
interface |
SparseMatrix
A sparse matrix stores only non-zero values.
|
Modifier and Type | Class and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
LILSparseMatrix.add(Matrix that) |
Matrix |
DOKSparseMatrix.add(Matrix that) |
Matrix |
CSRSparseMatrix.add(Matrix that) |
Matrix |
LILSparseMatrix.minus(Matrix that) |
Matrix |
DOKSparseMatrix.minus(Matrix that) |
Matrix |
CSRSparseMatrix.minus(Matrix that) |
Matrix |
LILSparseMatrix.multiply(Matrix that) |
Matrix |
DOKSparseMatrix.multiply(Matrix that) |
Matrix |
CSRSparseMatrix.multiply(Matrix that) |
Modifier and Type | Method and Description |
---|---|
Matrix |
LILSparseMatrix.add(Matrix that) |
Matrix |
DOKSparseMatrix.add(Matrix that) |
Matrix |
CSRSparseMatrix.add(Matrix that) |
Vector |
SparseVector.leftMultiply(Matrix A)
Left multiplies a matrix.
|
Matrix |
LILSparseMatrix.minus(Matrix that) |
Matrix |
DOKSparseMatrix.minus(Matrix that) |
Matrix |
CSRSparseMatrix.minus(Matrix that) |
Matrix |
LILSparseMatrix.multiply(Matrix that) |
Matrix |
DOKSparseMatrix.multiply(Matrix that) |
Matrix |
CSRSparseMatrix.multiply(Matrix that) |
Constructor and Description |
---|
CSRSparseMatrix(Matrix A)
Constructs a sparse matrix from a matrix.
|
Modifier and Type | Method and Description |
---|---|
Preconditioner |
PreconditionerFactory.newInstance(Matrix A)
Construct a new instance of
Preconditioner for a coefficient matrix. |
Constructor and Description |
---|
JacobiPreconditioner(Matrix A)
Construct a Jacobi preconditioner.
|
SSORPreconditioner(Matrix A,
double omega)
Construct an SSOR preconditioner with a symmetric coefficient matrix.
|
Constructor and Description |
---|
SORSweep(Matrix A,
Vector b,
double omega)
Construct an instance to perform forward or backward sweep for a linear
system Ax = b.
|
Modifier and Type | Class and 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'AP
|
class |
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, satisfies
A.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
where
|
class |
MAT
MAT is the inverse operator of SVEC . |
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-1AP
|
class |
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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
FastKroneckerProduct.add(Matrix that) |
Matrix |
ColumnBindMatrix.add(Matrix that) |
Matrix |
DiagonalSum.add(Matrix that) |
Matrix |
SubMatrixRef.add(Matrix that) |
static Matrix |
MatrixFactory.cbind(List<Vector> vectors)
Combines a list of vectors by columns.
|
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.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 |
FastKroneckerProduct.minus(Matrix that) |
Matrix |
ColumnBindMatrix.minus(Matrix that) |
Matrix |
DiagonalSum.minus(Matrix that) |
Matrix |
SubMatrixRef.minus(Matrix that) |
Matrix |
FastKroneckerProduct.multiply(Matrix that) |
Matrix |
ColumnBindMatrix.multiply(Matrix that) |
Matrix |
DiagonalSum.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 |
FastKroneckerProduct.opposite() |
Matrix |
ColumnBindMatrix.opposite() |
Matrix |
DiagonalSum.opposite() |
Matrix |
SubMatrixRef.opposite() |
static Matrix |
MatrixFactory.randomPositiveDefiniteMatrix(int dim,
RandomNumberGenerator rng)
Constructs a random symmetric, positive definite matrix.
|
static Matrix |
MatrixFactory.rbind(List<Vector> vectors)
Combines a list of array of vectors by rows.
|
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.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 |
FastKroneckerProduct.scaled(double scalar) |
Matrix |
ColumnBindMatrix.scaled(double c) |
Matrix |
DiagonalSum.scaled(double c) |
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 |
FastKroneckerProduct.t() |
Matrix |
ColumnBindMatrix.t() |
Matrix |
DiagonalSum.t() |
Matrix |
SubMatrixRef.t() |
Matrix |
ElementaryOperation.T()
Get the transformed matrix T.
|
Matrix |
ColumnBindMatrix.ZERO() |
Matrix |
DiagonalSum.ZERO() |
Matrix |
SubMatrixRef.ZERO() |
Modifier and Type | Method and Description |
---|---|
Matrix |
FastKroneckerProduct.add(Matrix that) |
Matrix |
ColumnBindMatrix.add(Matrix that) |
Matrix |
DiagonalSum.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 |
FastKroneckerProduct.minus(Matrix that) |
Matrix |
ColumnBindMatrix.minus(Matrix that) |
Matrix |
DiagonalSum.minus(Matrix that) |
Matrix |
SubMatrixRef.minus(Matrix that) |
Matrix |
FastKroneckerProduct.multiply(Matrix that) |
Matrix |
ColumnBindMatrix.multiply(Matrix that) |
Matrix |
DiagonalSum.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.
|
Constructor and 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 * B
|
Pow(Matrix A,
int n,
double base)
Construct the power matrix An so that
An = basescale * B
|
PseudoInverse(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 \(b'Xb\).
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Class and 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.
|
Constructor and 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.
|
Constructor and Description |
---|
DenseVector(Matrix A)
Constructs a vector from a column or row matrix.
|
Modifier and Type | Method and 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 a
RealScalarFunction 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 a
RealScalarFunction 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.
|
Constructor and 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).
|
Modifier and Type | Class and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
JacobianFunction.evaluate(Vector x) |
Matrix |
HessianFunction.evaluate(Vector x) |
Modifier and Type | Method and Description |
---|---|
abstract Matrix |
R1toMatrix.evaluate(double x)
Evaluate f(x) = A.
|
abstract Matrix |
R2toMatrix.evaluate(double x1,
double x2)
Evaluate f(x1, x2) = A.
|
Matrix |
R2toMatrix.evaluate(Vector x) |
Matrix |
R1toMatrix.evaluate(Vector x) |
Constructor and Description |
---|
R1toConstantMatrix(Matrix A)
Construct a constant matrix function.
|
Constructor and 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: \(f(x) = \frac{1}{2} \times x'Hx + x'p + c\).
|
Modifier and Type | Method and Description |
---|---|
Matrix |
LinearConstraints.getActiveConstraints(Vector x,
double epsilon)
Get the active constraint.
|
Constructor and 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.
|
Modifier and Type | Field and Description |
---|---|
protected Matrix |
PrimalDualPathFollowingMinimizer.Solution.A
This is either [A] or
[ A]
[-C]
|
protected Matrix |
PrimalDualPathFollowingMinimizer.Solution.I |
Modifier and Type | Method and Description |
---|---|
Matrix |
Hp.evaluate(Matrix U)
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
|
protected Matrix |
PrimalDualPathFollowingMinimizer.Solution.svecA() |
protected Matrix |
HomogeneousPathFollowingMinimizer.Solution.svecA()
Computes A^ in "Toh, Todd, Tütüncü, Section 3.1".
|
protected Matrix |
CSDPMinimizer.Solution.svecA() |
Modifier and Type | Method and Description |
---|---|
Matrix |
Hp.evaluate(Matrix U)
Computes \(H_p(U) = \frac{1}{2}[PUP^{-1}]+P^{-*}U^*P^*\).
|
protected static double |
PrimalDualPathFollowingMinimizer.getMinEigenValue(Matrix A,
double epsilon)
Gets the minimum of all the eigen values 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 |
CSDPMinimizer.getMinEigenValue(Matrix A,
double epsilon)
Gets the minimum of all the eigenvalues of a matrix.
|
Constructor and Description |
---|
CentralPath(Matrix X,
Vector y,
Matrix S)
Construct a central path.
|
Hp(Matrix P)
Constructs a symmetrization operator.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SOCPDualProblem.A()
\[
A = [A_1, A_2, ...
|
Matrix |
SOCPGeneralConstraint.A()
Gets A.
|
Matrix |
SOCPDualProblem.A(int i)
Gets Ai.
|
Constructor and Description |
---|
EqualityConstraints(Vector b,
Matrix[] A,
Vector[] c)
Constructs the equality constraints for a dual SOCP problem, \(\max_y \mathbf{b'y}
\textrm{ s.t.,} \\ \mathbf{\hat{A}_i'y + s_i = \hat{c}_i} \\ s_i \in K_i, i = 1, 2, ...,
q\).
|
SOCPDualProblem(Vector b,
Matrix[] A,
Vector[] c)
Constructs a dual SOCP problem.
|
SOCPGeneralConstraint(Matrix A,
Vector c,
Vector b,
double d)
Constructs a SOCP general constraint.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
PortfolioRiskExactSigma.MatrixRoot.getRoot(Matrix A)
Gets the root of a matrix
|
Matrix |
PortfolioRiskExactSigma.DefaultRoot.getRoot(Matrix A) |
Matrix |
PortfolioRiskExactSigma.Diagonalization.getRoot(Matrix A) |
Matrix |
PortfolioRiskExactSigma.root() |
Matrix |
PortfolioRiskExactSigma.Sigma() |
abstract Matrix |
SOCPRiskConstraint.Sigma() |
Modifier and Type | Method and Description |
---|---|
Matrix |
PortfolioRiskExactSigma.MatrixRoot.getRoot(Matrix A)
Gets the root of a matrix
|
Matrix |
PortfolioRiskExactSigma.DefaultRoot.getRoot(Matrix A) |
Matrix |
PortfolioRiskExactSigma.Diagonalization.getRoot(Matrix A) |
Constructor and 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.
|
Constructor and 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.
|
Modifier and Type | Method and 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) |
Constructor and Description |
---|
Problem(Matrix A,
Vector b,
Vector c) |
Modifier and Type | Method and Description |
---|---|
Matrix |
QPProblemOnlyEqualityConstraints.getAplus() |
Matrix |
QPProblemOnlyEqualityConstraints.getVr() |
Modifier and Type | Method and Description |
---|---|
Matrix |
SQPASVariation1.getInitialHessian(Vector x0,
Vector v0,
Vector u0) |
Matrix |
SQPASVariation.getInitialHessian(Vector x0,
Vector v0,
Vector u0)
Get the initial Hessian matrix.
|
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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SQPASEVariation1.getInitialHessian(Vector x0,
Vector v0) |
Matrix |
SQPASEVariation.getInitialHessian(Vector x0,
Vector u0)
Get the initial Hessian matrix.
|
Matrix |
SQPASEVariation2.updateHessian(Vector x1,
Vector v1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0) |
Matrix |
SQPASEVariation1.updateHessian(Vector x1,
Vector v1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0) |
Matrix |
SQPASEVariation.updateHessian(Vector x1,
Vector u1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0)
Update the Hessian matrix using the latest iterates.
|
protected Matrix |
SQPASEVariation1.W(Vector x,
Vector u)
Compute W.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
SQPASEVariation2.updateHessian(Vector x1,
Vector v1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0) |
Matrix |
SQPASEVariation1.updateHessian(Vector x1,
Vector v1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0) |
Matrix |
SQPASEVariation.updateHessian(Vector x1,
Vector u1,
Vector d,
Vector g0,
Matrix A0,
Matrix W0)
Update the Hessian matrix using the latest iterates.
|
Modifier and Type | Field and Description |
---|---|
protected Matrix |
QuasiNewtonMinimizer.QuasiNewtonImpl.Sk
This is the approximate inverse of the Hessian matrix.
|
Modifier and Type | Method and 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'Sγ
|
static 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-1
|
static 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.
|
Modifier and Type | Method and 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'Sγ
|
static 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-1
|
static 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
CointegrationMLE.alpha()
Get the set of adjusting coefficients, by columns.
|
Matrix |
CointegrationMLE.beta()
Get the set of cointegrating factors, by columns.
|
Constructor and Description |
---|
CointegrationMLE(MultivariateSimpleTimeSeries ts,
boolean intercept,
int p,
Matrix D)
Perform the Johansen MLE procedure on a multivariate time series.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
LedoitWolf2004.Result.getCovarianceMatrix()
Gets the "shrunk" covariance matrix.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Constructor and Description |
---|
CovarianceSelectionProblem(Matrix S,
double t)
Constructs a covariance selection problem.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
CovarianceSelectionLASSO.covariance()
Get the estimated covariance matrix.
|
Matrix |
CovarianceSelectionGLASSOFAST.covariance()
Gets the estimated covariance matrix.
|
Matrix |
CovarianceSelectionLASSO.inverseCovariance()
Get the inverse of the estimated covariance matrix.
|
Matrix |
CovarianceSelectionGLASSOFAST.inverseCovariance()
Gets the inverse of the estimated covariance matrix.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
LedoitWolf2016.Result.getShrunkCovarianceMatrix()
Gets the nonlinear shrinkage covariance matrix.
|
Modifier and Type | Method and Description |
---|---|
LedoitWolf2016.Result |
LedoitWolf2016.estimate(Matrix Y) |
Modifier and Type | Field and Description |
---|---|
Matrix |
QuEST.Result.lambda_Jacobian
lambda Jacobian
|
Modifier and Type | Class and 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.
|
Constructor and Description |
---|
CorrelationMatrix(Matrix cov)
Construct a correlation matrix from a covariance matrix.
|
Modifier and Type | Class and 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.
|
Constructor and Description |
---|
SampleCovariance(Matrix A)
Construct the covariance matrix of a matrix.
|
SampleCovariance(Matrix A,
boolean unbiased)
Construct the covariance matrix of a matrix.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
MultivariateProbabilityDistribution.covariance()
Gets the covariance matrix of this distribution.
|
Matrix |
DirichletDistribution.covariance() |
Matrix |
MultivariateNormalDistribution.covariance() |
Matrix |
MultivariateTDistribution.covariance() |
Matrix |
MultinomialDistribution.covariance() |
Constructor and 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.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
AbstractBivariateEVD.covariance() |
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Method and 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.
|
Constructor and Description |
---|
AverageImplicitModelPCA(Matrix R,
double varExplained)
Constructs an explicit-implicit model for a time series of vectored
observations
|
AverageImplicitModelPCA(Matrix R,
int K)
Constructs an explicit-implicit model for a time series of vectored
observations
|
ExplicitImplicitModelPCA(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 threshold
|
ImplicitModelPCA(Matrix R,
int K)
Constructs an implicit-model that will have K implicit factors.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
PCA.loadings()
Gets the matrix of variable loadings.
|
Matrix |
PCAbySVD.loadings() |
Matrix |
PCAbyEigen.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.
|
Constructor and 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.
|
Constructor and Description |
---|
HiddenMarkovModel(Vector PI,
Matrix A,
RandomNumberGenerator[] B) |
HMMRNG(Vector PI,
Matrix A,
RandomNumberGenerator[] B)
Constructs a hidden Markov model.
|
Modifier and Type | Method and 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).
|
Modifier and Type | Method and 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).
|
Constructor and Description |
---|
DiscreteHMM(Vector PI,
Matrix A,
Matrix B)
Constructs a discrete hidden Markov model.
|
Constructor and Description |
---|
MixtureHMM(Vector PI,
Matrix A,
MixtureDistribution dist)
Constructs a mixture hidden Markov model.
|
Modifier and Type | Method and 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).
|
Constructor and Description |
---|
SimpleMC(Vector PI,
Matrix A)
Constructs a time-homogeneous Markov chain with a finite state space.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
RobustAdaptiveMetropolis.S()
Gets the tuned scaling matrix (this changes each time a new sample is drawn).
|
Constructor and 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.
|
Constructor and Description |
---|
GaussianProposalFunction(Matrix scale,
RandomLongGenerator uniform)
Constructs a Gaussian proposal function.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
MultivariateResampler.newResample()
Gets a resample from the original sample.
|
Matrix |
GroupResampler.newResample() |
Constructor and 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.
|
Constructor and Description |
---|
LMProblem(Vector y,
Matrix X)
Constructs a linear regression problem, assuming
a constant term (the intercept)
equal weights assigned to all observations
|
LMProblem(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).
|
Constructor and Description |
---|
GLMProblem(Vector y,
Matrix X,
boolean intercept,
GLMFamily family)
Construct a GLM problem.
|
Modifier and Type | Method and Description |
---|---|
protected Matrix |
GLMModelSelection.subA()
Constructs a covariates subset.
|
Modifier and Type | Method and Description |
---|---|
int |
BackwardElimination.Step.eliminate(GLMProblem problem,
Matrix subA) |
int |
EliminationByZValue.eliminate(GLMProblem problem,
Matrix subA) |
int |
EliminationByAIC.eliminate(GLMProblem problem,
Matrix subA) |
int |
SelectionByZValue.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
int |
ForwardSelection.Step.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
int |
SelectionByAIC.select(GLMProblem problem,
Matrix subA,
int[] factorChoices) |
Constructor and Description |
---|
QuasiGLMProblem(Vector y,
Matrix X,
boolean intercept,
QuasiFamily quasiFamily)
Constructs a quasi GLM problem.
|
Constructor and 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.
|
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
DiffusionMatrix.evaluate(MultivariateFt ft)
Evaluate the diffusion matrix, σ(dt, Xt, Zt, ...), with respect to a filtration.
|
Matrix |
ConstantSigma1.evaluate(MultivariateFt ft) |
Matrix |
DiffusionSigma.evaluate(MultivariateFt ft) |
Constructor and Description |
---|
ConstantSigma1(Matrix sigma)
Construct a constant diffusion coefficient function.
|
ConstantSigma2(Matrix sigma)
Deprecated.
Construct a constant diffusion coefficient function.
|
Constructor and Description |
---|
MultivariateBrownianSDE(Vector mu,
Matrix sigma)
Construct a multi-dimensional Brownian motion.
|
Modifier and Type | Method and Description |
---|---|
static Matrix |
ChiSquareIndependenceTest.getExpectedContingencyTable(int[] rowSums,
int[] colSums)
Assume the null hypothesis of independence, we compute the expected frequency of each
category.
|
Modifier and Type | Method and 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.
|
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
MultivariateTimeSeries.toMatrix()
Convert this multivariate time series into an m x n matrix, where
m is the dimension, and n the length.
|
Matrix |
MultivariateGenericTimeTimeSeries.toMatrix() |
Constructor and Description |
---|
MultivariateGenericTimeTimeSeries(T[] timestamps,
Matrix values)
Construct a multivariate time series from timestamps and vectors.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
MultivariateSimpleTimeSeries.toMatrix() |
Constructor and Description |
---|
MultivariateSimpleTimeSeries(Matrix values)
Construct an instance of
MultivariateSimpleTimeSeries . |
Modifier and Type | Method and Description |
---|---|
static Matrix |
UnivariateTimeSeriesUtils.toMatrix(UnivariateTimeSeries<?,?> ts)
Cast a time series into a column matrix, discarding the timestamps.
|
Modifier and Type | Method and 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.
|
Constructor and 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)
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(Matrix[] phi,
int d,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARIMA model with 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)
Construct a multivariate ARIMA model with unit variance.
|
VARIMAModel(Vector mu,
Matrix[] phi,
int d,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARIMA model.
|
VARIMAModel(Vector mu,
Matrix[] phi,
int d,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARIMA model.
|
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)
Construct a multivariate ARIMAX model with unit variance and zero-intercept (mu).
|
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(Matrix[] phi,
int d,
Matrix[] theta,
Matrix psi,
Matrix sigma)
Construct a multivariate ARIMAX model with 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)
Construct a multivariate ARIMAX model with unit variance.
|
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.
|
VARIMAXModel(Vector mu,
Matrix[] phi,
int d,
Matrix[] theta,
Matrix psi,
Matrix sigma)
Construct a multivariate ARIMAX model.
|
VARIMAXModel(Vector mu,
Matrix[] phi,
int d,
Matrix[] theta,
Matrix psi,
Matrix sigma)
Construct a multivariate ARIMAX model.
|
Modifier and Type | Method and 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 |
VARMAAutoCovariance.evaluate(double i)
Get the i-th auto-covariance matrix.
|
Matrix |
VARMAAutoCorrelation.evaluate(double i,
double j) |
Matrix |
VARMAAutoCovariance.evaluate(double i,
double j) |
Matrix[] |
VMAInvertibility.PI()
Get the coefficients of the linear representation of the time series.
|
Modifier and Type | Method and 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.
|
Constructor and Description |
---|
VARMAModel(Matrix[] phi,
Matrix[] theta)
Construct a multivariate ARMA model with unit variance and zero-intercept (mu).
|
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(Matrix[] phi,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARMA model with 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)
Construct a multivariate ARMA model with unit variance.
|
VARMAModel(Vector mu,
Matrix[] phi,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARMA model.
|
VARMAModel(Vector mu,
Matrix[] phi,
Matrix[] theta,
Matrix sigma)
Construct a multivariate ARMA model.
|
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)
Construct a multivariate ARMAX model with unit variance and zero-intercept (mu).
|
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(Matrix[] phi,
Matrix[] theta,
Matrix psi,
Matrix sigma)
Construct a multivariate ARMAX model with 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)
Construct a multivariate ARMAX model with unit variance.
|
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.
|
VARMAXModel(Vector mu,
Matrix[] phi,
Matrix[] theta,
Matrix psi,
Matrix sigma)
Construct a multivariate ARMAX model.
|
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(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.
|
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)
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(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)
Construct a VARX model with unit variance.
|
VARXModel(Vector mu,
Matrix[] phi,
Matrix psi,
Matrix sigma)
Construct a VARX model.
|
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.
|
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(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.
|
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(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.
|
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(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.
|
VMAModel(Vector mu,
Matrix[] theta,
Matrix sigma)
Construct a multivariate MA model.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and Description |
---|---|
Matrix |
Infantino2010PCA.Signal.B() |
Matrix |
Infantino2010PCA.Signal.D() |
Matrix |
Infantino2010PCA.Signal.X() |
Modifier and Type | Method and 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 last
nRows rows. |
Modifier and Type | Field and Description |
---|---|
protected Matrix |
TradingPair.returns |
Modifier and Type | Method and 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) |
Modifier and Type | Method and 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> |
PairingModel2.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
List<TradingPair> |
PairingModel.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
List<TradingPair> |
PairingModel4.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
List<TradingPair> |
PairingModel1.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
List<TradingPair> |
PairingModel3.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
List<TradingPair> |
PairingModel5.getTradingPairs(List<String> allSymbols,
List<String> tradableSymbols,
Matrix prices) |
Modifier and Type | Method and Description |
---|---|
static double |
CorrelationCheck.cor(Matrix A,
double tailed) |
Constructor and Description |
---|
CorrelationCheck(Matrix A,
double lowerBound,
double upperBound) |
CorrelationCheck(Matrix A,
double tail,
double lowerBound,
double upperBound) |
Modifier and Type | Method and Description |
---|---|
Matrix |
AhatEstimation.compute() |
Constructor and 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.
|
Modifier and Type | Method and Description |
---|---|
default Matrix |
PortfolioOptimizationAlgorithm.CovarianceEstimator.getCovariances(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Matrix |
PortfolioOptimizationAlgorithm.SampleCovarianceEstimator.getCovariances(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Modifier and Type | Method and Description |
---|---|
default Matrix |
PortfolioOptimizationAlgorithm.CovarianceEstimator.getCovariances(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Matrix |
PortfolioOptimizationAlgorithm.SampleCovarianceEstimator.getCovariances(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
default Vector |
PortfolioOptimizationAlgorithm.MeanEstimator.getMeans(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Vector |
PortfolioOptimizationAlgorithm.SampleMeanEstimator.getMeans(Matrix returns,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Vector |
TopNOptimizationAlgorithm.getOptimalWeights(Matrix returns,
Vector weights0,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Vector |
Lai2010OptimizationAlgorithm.getOptimalWeights(Matrix returns,
Vector weights0,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Vector |
PortfolioOptimizationAlgorithm.getOptimalWeights(Matrix returns,
Vector weights0,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval)
Computes the optimal weights for the products using returns.
|
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.
|
Constructor and 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.
|
Modifier and Type | Method and 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.
|
Modifier and Type | Method and Description |
---|---|
Lai2010NPEBModel.OptimalWeights |
Lai2010NPEBModel.optimalWeights(Matrix returns,
double lambda)
Computes the weights based on given historical returns and the
risk-aversion index λ.
|
Constructor and Description |
---|
NPEBPortfolioMomentsEstimator(Matrix returns,
ReturnsMoments.Estimator returnMomentsEstimator,
MVOptimizer mvOptimizer,
ReturnsResamplerFactory resamplerFactory,
int nBootstrapSamples) |
Modifier and Type | Field and Description |
---|---|
Matrix |
GARCHResamplerFactory2.sigma2 |
Matrix |
GARCHResamplerFactory2.stdinno |
Modifier and Type | Method and Description |
---|---|
protected abstract ResamplerModel |
ModelResamplerFactory.getResamplerModel(Matrix returns) |
protected ResamplerModel |
ARResamplerFactory.getResamplerModel(Matrix returns) |
protected ResamplerModel |
GARCHResamplerFactory.getResamplerModel(Matrix returns) |
MultivariateResampler |
ModelResamplerFactory.newResampler(Matrix returns) |
MultivariateResampler |
GroupResamplerFactory.newResampler(Matrix returns) |
MultivariateResampler |
ReturnsResamplerFactory.newResampler(Matrix returns)
Constructs a new instance of a re-sampling mechanism.
|
MultivariateResampler |
GARCHResamplerFactory2.newResampler(Matrix returns) |
Modifier and Type | Method and Description |
---|---|
Matrix |
SimpleGARCHFit.fittedValues() |
Matrix |
ResamplerModel.fittedValues() |
Matrix |
SimpleAR1Fit.fittedValues() |
static Matrix |
ResamplerModel.fittedValues(OLSResiduals[] residuals) |
Matrix |
SimpleGARCHFit.sigma2() |
Matrix |
ResamplerModel.sigma2()
Gets the conditional variances of residuals over time.
|
Matrix |
SimpleAR1Fit.sigma2() |
Matrix |
SimpleGARCHFit.standarizedInnovations() |
Matrix |
ResamplerModel.standarizedInnovations()
Gets the standarized innovations (normalized by the conditional standard
deviation at the time) of the time series.
|
Matrix |
SimpleAR1Fit.standarizedInnovations() |
Modifier and Type | Method and Description |
---|---|
ReturnsMoments |
SimpleGARCHMoments2.estimate(Matrix stdinno) |
ReturnsMoments |
SimpleAR1Moments.estimate(Matrix returns) |
ReturnsMoments |
SimpleGARCHMoments1.estimate(Matrix returns) |
Constructor and Description |
---|
SimpleAR1Fit(Matrix mts) |
SimpleGARCHFit(Matrix mts) |
Modifier and Type | Method and Description |
---|---|
Vector |
MVOptimizerMinWeights.optimalWeights(Vector mu,
Matrix V,
double lambda,
double eta) |
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 |
MVOptimizerShrankMean.optimalWeights(Vector mu0,
Matrix V,
double lambda,
double eta) |
Vector |
MVOptimizerNoConstraint.optimalWeights(Vector mu,
Matrix V,
double lambda,
double eta) |
Constructor and 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,
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.
|
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.
|
Modifier and Type | Method and Description |
---|---|
Vector |
NMSAAM.getOptimalWeights(Matrix returns,
Vector weights0,
PortfolioOptimizationAlgorithm.SymbolLookup symbolLookup,
org.joda.time.Interval interval) |
Constructor and 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.
|
Constructor and 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.
|
Modifier and Type | Class and Description |
---|---|
class |
ReturnsMatrix |
Modifier and Type | Method and Description |
---|---|
static Vector |
Returns.getMeanReturns(Matrix returns)
Computes a vector of mean returns of the input returns (one column for
one asset).
|
Constructor and Description |
---|
ReturnsMatrix(Matrix prices) |
ReturnsMatrix(Matrix prices,
ReturnsCalculator calc) |
Modifier and Type | Method and Description |
---|---|
Matrix |
ReturnsMoments.V()
Gets the second moment matrix.
|
Modifier and Type | Method and Description |
---|---|
ReturnsMoments |
MomentsEstimatorLedoitWolf.estimate(Matrix returns) |
ReturnsMoments |
ReturnsMoments.Estimator.estimate(Matrix returns)
Estimates the moments from a given returns matrix.
|
Constructor and Description |
---|
ReturnsMoments(Vector mu,
Matrix V)
Constructs an instance.
|
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