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#Com.numericalmethod.suanshu.algebra.linear.matrix.doubles Classes and Interfaces - 170 results found.
| Name | Description | Type | Package | Framework |
| AutoParallelMatrixMathOperation | This class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
| BackwardSubstitution | Backward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| BiconjugateGradientSolver | The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| BiconjugateGradientStabilizedSolver | The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| BiDiagonalization | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu | |
| BiDiagonalizationByGolubKahanLanczos | This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
| BiDiagonalizationByHouseholder | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu | |
| BidiagonalMatrix | A bi-diagonal matrix is either upper or lower diagonal. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
| BidiagonalMatrix .BidiagonalMatrixType | the available types of bi-diagonal matricesa lower bi-diagonal matrix, where there are only non-zero entries on the main and sub | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
| BidiagonalSVDbyMR3 | Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 | SuanShu |
| BlockSplitPointSearch | Computes the splitting points with the given threshold. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| BlockWinogradAlgorithm | This implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
| CharacteristicPolynomial | The characteristic polynomial of a square matrix is the function The zeros of this polynomial are the eigenvalues of A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| Chol | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| Cholesky | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| CholeskyBanachiewicz | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| CholeskyBanachiewiczParallelized | This is a parallelized version of CholeskyBanachiewicz. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| CholeskySparse | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| CholeskyWang2006 | Cholesky decomposition works only for a positive definite matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
| ColumnBindMatrix | A fast "cbind" matrix from vectors. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| CongruentMatrix | Given a matrix A and an invertible matrix P, we create the congruent matrixSee Also:Wikipedia: Matrix congruence | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| ConjugateGradientNormalErrorSolver | For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| ConjugateGradientNormalResidualSolver | For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| ConjugateGradientSolver | The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| ConjugateGradientSquaredSolver | The Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| ConvergenceFailure | This exception is thrown by IterativeLinearSystemSolver. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
| ConvergenceFailure .Reason | the reasons for the convergence failureThrown when the iterative algorithm fails to proceed during its | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
| CSRSparseMatrix | The Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| DefaultDeflationCriterion .MatrixNorm | Computes the norm of a given matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| DefaultMatrixStorage | There are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
| Deflation | A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| DeflationCriterion | Determines whether a sub-diagonal entry is sufficiently small to be neglected. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| DenseData | This 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[][]. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
| DenseMatrix | This class implements the standard, dense, double based matrix representation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
| DenseMatrixMultiplication | Matrix operation that multiplies two matrices. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
| DenseMatrixMultiplicationByBlock | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu | |
| DenseMatrixMultiplicationByBlock .BlockAlgorithm | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu | |
| DenseMatrixMultiplicationByIjk | parallel execution with multiple threads. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
| Densifiable | This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
| DiagonalMatrix | A diagonal matrix has non-zero entries only on the main diagonal. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
| DiagonalSum | Add diagonal elements to a matrix, an efficient implementation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| DOKSparseMatrix | The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| Doolittle | Doolittle 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, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
| DQDS | Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the qd-array Z to high relative accuracy. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
| Eigen | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu | |
| Eigen .Method | the available methods to compute eigenvalues and eigenvectorsFor a matrix of dimension 4 or smaller. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| EigenBoundUtils | Utility methods for computing bounds of eigenvalues. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| EigenCount | Counts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than aSee Also:"W. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| EigenCountInRange | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu | |
| EigenDecomposition | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu | |
| EigenProperty | EigenProperty is a read-only structure that contains the information about a particular eigenvalue, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| EigenvalueByDQDS | Computes all the eigenvalues of a symmetric tridiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
| ElementaryOperation | There are three elementary row operations which are equivalent to left multiplying an elementary They are row switching, row multiplication, and row addition. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| FastKroneckerProduct | This is a fast and memory-saving implementation of computing the Kronecker product. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| ForwardSubstitution | Forward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| GaussianElimination | The Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
| GaussianElimination4SquareMatrix | This is a wrapper for GaussianElimination but applies only to square matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
| GaussJordanElimination | Gauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
| GaussSeidelSolver | Similar to the Jacobi method, the Gauss-Seidel method (GS) solves each equation in sequential order. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
| GeneralizedConjugateResidualSolver | The Generalized Conjugate Residual method (GCR) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| GeneralizedMinimalResidualSolver | The Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| Getvec | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec | SuanShu | |
| GivensMatrix | Givens rotation is a rotation in the plane spanned by two coordinates axes. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
| GoldfeldQuandtTrotter | Goldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
| GolubKahanSVD | Golub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
| GramSchmidt | The Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
| Hessenberg | An upper Hessenberg matrix is a square matrix which has zero entries below the first 0 & 9 & 10 & 11 & \ | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| HessenbergDecomposition | Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| HessenbergDeflationSearch | Given a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| HilbertMatrix | A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1) | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
| Householder4SubVector | Faster implementation of Householder reflection for sub-vectors at a given index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
| Householder4ZeroGenerator | Faster implementation of Householder reflection for zero generator vector. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
| HouseholderContext | This is the context information about a Householder transformation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
| HouseholderInPlace | Maintains the matrix to be transformed by a sequence of Householder reflections. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
| HouseholderInPlace .Householder | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu | |
| HouseholderQR | Successive Householder reflections gradually transform a matrix A to the upper triangular For example, the first step is to multiply A with a Householder matrix | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
| HouseholderReflection | A 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, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
| IdentityPreconditioner | This identity preconditioner is used when no preconditioning is applied. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
| ImmutableMatrix | This is a read-only view of a Matrix instance. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
| InnerProduct | The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| Inverse | For a square matrix A, the inverse, A-1, if exists, satisfies A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| InverseIteration | Inverse iteration is an iterative eigenvalue algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| InverseIteration .StoppingCriterion | This interface defines the convergence criterion. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| IterativeLinearSystemSolver | An iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
| IterativeLinearSystemSolver .Solution | This is the solution to a system of linear equations using an iterativeSearch for a solution that optimizes the objective function from the | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
| JacobiPreconditioner | The Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal of | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
| JacobiSolver | The Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
| Kernel | The kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| Kernel .Method | These are the available methods to compute kernel basis. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| 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 | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| LDDecomposition | Represents a L D LT decomposition of a shifted symmetric tridiagonal matrix where T is a symmetric tridiagonal matrix, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| LDFactorizationFromRoot | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu | |
| LDLt | The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
| LILSparseMatrix | The list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| LinearSystemSolver | Solve a system of linear equations in the form: We assume that, after row reduction, A has no more rows than columns. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| LinearSystemSolver .NoSolution | This is the runtime exception thrown when it fails to solve a system of linearSee Also:Serialized Form | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| LinearSystemSolver .Solution | This is the solution to a linear system of equations. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| LowerTriangularMatrix | A lower triangular matrix has 0 entries where column index > row index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
| LSProblem | This is the problem of solving a system of linear equations. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| LU | LU decomposition decomposes an n x n matrix A so that P * A = L * U. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
| LUDecomposition | LU decomposition decomposes an n x n matrix A so that P * A = L * U. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
| LUSolver | Use LU decomposition to solve Ax = b where A is square and The dimensions of A and b must match. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| MAT | MAT is the inverse operator of SVEC. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| Matrix | This interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
| MatrixAccess | This interface defines the methods for accessing entries in a matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
| MatrixCoordinate | The 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. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| MatrixFactory | These are the utility functions to create a new matrix/vector from existing ones. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| MatrixMathOperation | This interface defines some standard operations for generic matrices. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
| MatrixMeasure | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu | |
| MatrixPropertyUtils | These are the boolean operators that take matrices or vectors and check if they satisfy aChecks if all matrices are SparseMatrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
| MatrixRing | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu | |
| MatrixRootByDiagonalization | The square root of a matrix extends the notion of square root from numbers to matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| MatrixTable | A matrix is represented by a rectangular table structure with accessors. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
| MatrixUtils | These are the utility functions to apply to matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| MatthewsDavies | Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
| MinimalResidualSolver | The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| MR3 | Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| OLSSolver | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| OLSSolverByQR | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| OLSSolverBySVD | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| OuterProduct | The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| PermutationMatrix | A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
| 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, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
| 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. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
| Pow | This is a square matrix A to the power of an integer n, An. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| Preconditioner | Preconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergence | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
| PreconditionerFactory | This constructs a new instance of Preconditioner for a coefficient matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
| PseudoInverse | The Moore-Penrose pseudo-inverse of an m x n matrix A is A+. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| QR | QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
| QRAlgorithm | The 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. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| QRDecomposition | QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
| QuasiMinimalResidualSolver | The Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| SimilarMatrix | Given a matrix A and an invertible matrix P, we construct the similar matrixSee Also:Wikipedia: Similar matrix | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| SimpleMatrixMathOperation | This is a generic, single-threaded implementation of matrix math operations. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
| SingularValueByDQDS | Computes all the singular values of a bidiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
| SORSweep | This is a building block for to perform the forward or backward sweep. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
| SparseMatrix | A sparse matrix stores only non-zero values. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseMatrix .Entry | This is a (non-zero) entry in a sparse matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseMatrix .Entry .TopLeftFirstComparator | This Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseMatrixUtils | These are the utility functions for SparseMatrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseStructure | This interface defines common operations on sparse structures such as sparse vector or sparse matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseVector | A sparse vector stores only non-zero values. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseVector .Entry | This is an entry in a SparseVector. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseVector .Entry .Comparator | This Comparator sorts the matrix coordinates first from top to bottom (rows), and then from left to right (columns). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| SparseVector .Iterator | This wrapper class overrides the Iterator. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
| Spectrum | A spectrum is the set of eigenvalues of a matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
| SSORPreconditioner | SSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed as | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
| SteepestDescentSolver | The Steepest Descent method (SDM) solves a symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
| SturmCount | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu | |
| SubMatrixBlock | Sub-matrix block representation for block algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
| SubMatrixRef | This is a 'reference' to a sub-matrix of a larger matrix without copying it. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| SuccessiveOverrelaxationSolver | The Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
| SVD | SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, U' * A * V = D, or U * D * V' = A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
| SVD .Method | Golub-Kahan, for higher precision. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
| SVDbyMR3 | Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 | SuanShu |
| SVDDecomposition | SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
| SVEC | SVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| SymmetricEigenByMR3 | Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
| SymmetricEigenFor2x2Matrix | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu | |
| SymmetricKronecker | Compute the symmetric Kronecker product of two matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
| SymmetricMatrix | A symmetric matrix is a square matrix such that its transpose equals to itself, i. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
| SymmetricQRAlgorithm | The symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| SymmetricSuccessiveOverrelaxationSolver | The Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in each | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
| SymmetricSVD | This algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
| ThomasAlgorithm | Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
| TridiagonalDeflationSearch | This class locates deflation in a tridiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
| TriDiagonalization | A 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 first | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
| TridiagonalMatrix | A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
| UpperTriangularMatrix | An upper triangular matrix has 0 entries where row index is greater than column index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
| VariancebtX | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu | |