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#Com.numericalmethod.suanshu.algebra.linear.matrix Classes and Interfaces - 178 results found.
NameDescriptionTypePackageFramework
AutoParallelMatrixMathOperationThis class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShu
BackwardSubstitutionBackward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
BiconjugateGradientSolverThe Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
BiconjugateGradientStabilizedSolverThe Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
BiDiagonalizationInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShu
BiDiagonalizationByGolubKahanLanczosThis implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShu
BiDiagonalizationByHouseholderClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShu
BidiagonalMatrixA bi-diagonal matrix is either upper or lower diagonal.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShu
BidiagonalMatrix .BidiagonalMatrixTypethe available types of bi-diagonal matricesa lower bi-diagonal matrix, where there are only non-zero entries on the main and subClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShu
BidiagonalSVDbyMR3Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3SuanShu
BlockSplitPointSearchComputes the splitting points with the given threshold.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
BlockWinogradAlgorithmThis implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
CharacteristicPolynomialThe characteristic polynomial of a square matrix is the function The zeros of this polynomial are the eigenvalues of A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
CholCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
CholeskyCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
CholeskyBanachiewiczCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
CholeskyBanachiewiczParallelizedThis is a parallelized version of CholeskyBanachiewicz.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
CholeskySparseCholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
CholeskyWang2006Cholesky decomposition works only for a positive definite matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.choleskySuanShu
ColumnBindMatrixA fast "cbind" matrix from vectors.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
ComplexMatrixThis is a Complex matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShu
CongruentMatrixGiven a matrix A and an invertible matrix P, we create the congruent matrixSee Also:Wikipedia: Matrix congruenceClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
ConjugateGradientNormalErrorSolverFor an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
ConjugateGradientNormalResidualSolverFor an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
ConjugateGradientSolverThe Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
ConjugateGradientSquaredSolverThe Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
ConvergenceFailureThis exception is thrown by IterativeLinearSystemSolver.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShu
ConvergenceFailure .Reasonthe reasons for the convergence failureThrown when the iterative algorithm fails to proceed during itsClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShu
CSRSparseMatrixThe Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
DefaultDeflationCriterion .MatrixNormComputes the norm of a given matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
DefaultMatrixStorageThere are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShu
DeflationA deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
DeflationCriterionDetermines whether a sub-diagonal entry is sufficiently small to be neglected.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
DenseDataThis implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D In general, computing index for a double[] is faster than that for a double[][].Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShu
DenseMatrixThis class implements the standard, dense, double based matrix representation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShu
DenseMatrixMultiplicationMatrix operation that multiplies two matrices.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
DenseMatrixMultiplicationByBlockClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
DenseMatrixMultiplicationByBlock .BlockAlgorithmInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
DenseMatrixMultiplicationByIjk parallel execution with multiple threads.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
DensifiableThis interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.denseSuanShu
DiagonalMatrixA diagonal matrix has non-zero entries only on the main diagonal.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShu
DiagonalSumAdd diagonal elements to a matrix, an efficient implementation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
DOKSparseMatrixThe Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
DoolittleDoolittle algorithm is a LU decomposition algorithm which decomposes a square matrix P is an n x n permutation matrix;L is an n x n (unit) lower triangular matrix;U is an n x n upper triangular matrix,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShu
DQDSComputes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the qd-array Z to high relative accuracy.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShu
EigenClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
Eigen .Methodthe available methods to compute eigenvalues and eigenvectorsFor a matrix of dimension 4 or smaller.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
EigenBoundUtilsUtility methods for computing bounds of eigenvalues.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
EigenCountCounts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than aSee Also:"W.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
EigenCountInRangeClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
EigenDecompositionClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
EigenPropertyEigenProperty is a read-only structure that contains the information about a particular eigenvalue,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
EigenvalueByDQDSComputes all the eigenvalues of a symmetric tridiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShu
ElementaryOperationThere are three elementary row operations which are equivalent to left multiplying an elementary They are row switching, row multiplication, and row addition.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
FastKroneckerProductThis is a fast and memory-saving implementation of computing the Kronecker product.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
ForwardSubstitutionForward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
GaussianEliminationThe Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShu
GaussianElimination4SquareMatrixThis is a wrapper for GaussianElimination but applies only to square matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShu
GaussJordanEliminationGauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianeliminationSuanShu
GaussSeidelSolverSimilar to the Jacobi method, the Gauss-Seidel method (GS) solves each equation in sequential order.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShu
GeneralizedConjugateResidualSolverThe Generalized Conjugate Residual method (GCR) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
GeneralizedMinimalResidualSolverThe Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
GenericFieldMatrixThis is a generic matrix over a Field.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShu
GenericMatrixThis class defines a matrix over a field.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.genericSuanShu
GenericMatrixAccessThis interface defines the methods for accessing entries in a matrix over a field.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.genericSuanShu
GetvecClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvecSuanShu
GivensMatrixGivens rotation is a rotation in the plane spanned by two coordinates axes.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShu
GoldfeldQuandtTrotterGoldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShu
GolubKahanSVDGolub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShu
GramSchmidtThe Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShu
HessenbergAn upper Hessenberg matrix is a square matrix which has zero entries below the first 0 & 9 & 10 & 11 & \Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
HessenbergDecompositionGiven a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
HessenbergDeflationSearchGiven a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
HilbertMatrixA Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1)Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShu
Householder4SubVectorFaster implementation of Householder reflection for sub-vectors at a given index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
Householder4ZeroGeneratorFaster implementation of Householder reflection for zero generator vector.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
HouseholderContextThis is the context information about a Householder transformation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
HouseholderInPlaceMaintains the matrix to be transformed by a sequence of Householder reflections.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
HouseholderInPlace .HouseholderClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
HouseholderQRSuccessive Householder reflections gradually transform a matrix A to the upper triangular For example, the first step is to multiply A with a Householder matrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShu
HouseholderReflectionA Householder transformation in the 3-dimensional space is the reflection of a vector in the The plane, containing the origin, is uniquely defined by a unit vector,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householderSuanShu
IdentityPreconditionerThis identity preconditioner is used when no preconditioning is applied.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShu
ImmutableMatrixThis is a read-only view of a Matrix instance.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
InnerProductThe Frobenius inner product is the component-wise inner product of two matrices as though they are vectors.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
InverseFor a square matrix A, the inverse, A-1, if exists, satisfies A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
InverseIterationInverse iteration is an iterative eigenvalue algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
InverseIteration .StoppingCriterionThis interface defines the convergence criterion.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
IterativeLinearSystemSolverAn iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShu
IterativeLinearSystemSolver .SolutionThis is the solution to a system of linear equations using an iterativeSearch for a solution that optimizes the objective function from theInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterativeSuanShu
JacobiPreconditionerThe Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal ofClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShu
JacobiSolverThe Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShu
KernelThe kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
Kernel .MethodThese are the available methods to compute kernel basis.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
KroneckerProductGiven an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, isClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
LDDecompositionRepresents a L D LT decomposition of a shifted symmetric tridiagonal matrix where T is a symmetric tridiagonal matrix,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
LDFactorizationFromRootClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
LDLtThe LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShu
LILSparseMatrixThe list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
LinearSystemSolverSolve a system of linear equations in the form: We assume that, after row reduction, A has no more rows than columns.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
LinearSystemSolver .NoSolutionThis is the runtime exception thrown when it fails to solve a system of linearSee Also:Serialized FormClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
LinearSystemSolver .SolutionThis is the solution to a linear system of equations.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
LowerTriangularMatrixA lower triangular matrix has 0 entries where column index > row index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShu
LSProblemThis is the problem of solving a system of linear equations.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
LULU decomposition decomposes an n x n matrix A so that P * A = L * U.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShu
LUDecompositionLU decomposition decomposes an n x n matrix A so that P * A = L * U.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangleSuanShu
LUSolverUse LU decomposition to solve Ax = b where A is square and The dimensions of A and b must match.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
MATMAT is the inverse operator of SVEC.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
MatrixThis interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
MatrixAccessThis interface defines the methods for accessing entries in a matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
MatrixAccessExceptionThis is the runtime exception thrown when trying to access an invalid entry in a matrix, e.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShu
MatrixCoordinateThe location of a matrix entry is specified by a 2D coordinates (i, j), where i and j are the row-index and column-index of the entry respectively.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
MatrixFactoryThese are the utility functions to create a new matrix/vector from existing ones.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
MatrixMathOperationThis interface defines some standard operations for generic matrices.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShu
MatrixMeasureClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
MatrixMismatchExceptionThis is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShu
MatrixPropertyUtilsThese are the boolean operators that take matrices or vectors and check if they satisfy aChecks if all matrices are SparseMatrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
MatrixRingInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
MatrixRootByDiagonalizationThe square root of a matrix extends the notion of square root from numbers to matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
MatrixSingularityExceptionThis is the runtime exception thrown when an operation acts on a singular matrix, e.Classcom.numericalmethod.suanshu.algebra.linear.matrixSuanShu
MatrixTableA matrix is represented by a rectangular table structure with accessors.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doublesSuanShu
MatrixUtilsThese are the utility functions to apply to matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
MatthewsDaviesMatthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShu
MinimalResidualSolverThe Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
MR3Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
OLSSolverThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
OLSSolverByQRThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
OLSSolverBySVDThis class solves an over-determined system of linear equations in the ordinary least square sense.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
OuterProductThe outer product of two vectors a and b, is a row vector multiplied on the left by a column vector.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
PermutationMatrixA permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtypeSuanShu
PositiveDefiniteMatrixByPositiveDiagonalThis 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,Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShu
PositiveSemiDefiniteMatrixNonNegativeDiagonalThis 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.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefiniteSuanShu
PowThis is a square matrix A to the power of an integer n, An.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
PreconditionerPreconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergenceInterfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShu
PreconditionerFactoryThis constructs a new instance of Preconditioner for a coefficient matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShu
PseudoInverseThe Moore-Penrose pseudo-inverse of an m x n matrix A is A+.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
QRQR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShu
QRAlgorithmThe QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a That is, Q'AQ = T where Q is orthogonal, and T is quasi-triangular.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
QRDecompositionQR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qrSuanShu
QuasiMinimalResidualSolverThe Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
RealMatrixThis is a Real matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtypeSuanShu
SimilarMatrixGiven a matrix A and an invertible matrix P, we construct the similar matrixSee Also:Wikipedia: Similar matrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
SimpleMatrixMathOperationThis is a generic, single-threaded implementation of matrix math operations.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperationSuanShu
SingularValueByDQDSComputes all the singular values of a bidiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqdsSuanShu
SORSweepThis is a building block for to perform the forward or backward sweep.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShu
SparseMatrixA sparse matrix stores only non-zero values.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseMatrix .EntryThis is a (non-zero) entry in a sparse matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseMatrix .Entry .TopLeftFirstComparatorThis Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseMatrixUtilsThese are the utility functions for SparseMatrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseStructureThis interface defines common operations on sparse structures such as sparse vector or sparse matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseVectorA sparse vector stores only non-zero values.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseVector .EntryThis is an entry in a SparseVector.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseVector .Entry .ComparatorThis Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SparseVector .IteratorThis wrapper class overrides the Iterator.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparseSuanShu
SpectrumA spectrum is the set of eigenvalues of a matrix.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigenSuanShu
SSORPreconditionerSSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed asClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditionerSuanShu
SteepestDescentSolverThe Steepest Descent method (SDM) solves a symmetric n-by-n linear system.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationarySuanShu
SturmCountClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
SubMatrixBlockSub-matrix block representation for block algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplicationSuanShu
SubMatrixRefThis is a 'reference' to a sub-matrix of a larger matrix without copying it.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
SuccessiveOverrelaxationSolverThe Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShu
SVDSVD decomposition decomposes a matrix A of dimension m x n, where m >= n, U' * A * V = D, or U * D * V' = A.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShu
SVD .MethodGolub-Kahan, for higher precision.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShu
SVDbyMR3Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3SuanShu
SVDDecompositionSVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A.Interfacecom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShu
SVECSVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
SymmetricEigenByMR3Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR).Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
SymmetricEigenFor2x2MatrixClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3SuanShu
SymmetricKroneckerCompute the symmetric Kronecker product of two matrices.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu
SymmetricMatrixA symmetric matrix is a square matrix such that its transpose equals to itself, i.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShu
SymmetricQRAlgorithmThe symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
SymmetricSuccessiveOverrelaxationSolverThe Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in eachClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationarySuanShu
SymmetricSVDThis algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svdSuanShu
ThomasAlgorithmThomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystemSuanShu
TridiagonalDeflationSearchThis class locates deflation in a tridiagonal matrix.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qrSuanShu
TriDiagonalizationA tri-diagonal matrix A is a matrix such that it has non-zero elements only in the main diagonal, the first diagonal below, and the firstClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalizationSuanShu
TridiagonalMatrixA tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonalSuanShu
UpperTriangularMatrixAn upper triangular matrix has 0 entries where row index is greater than column index.Classcom.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangleSuanShu
VariancebtXClasscom.numericalmethod.suanshu.algebra.linear.matrix.doubles.operationSuanShu