22.6 Function Reference

• ccolamd: Constrained column approximate minimum degree permutation.
• colamd: Column approximate minimum degree permutation.
• colperm: Returns the column permutations such that the columns of `S (:, P)' are ordered in terms of increase number of non-zero elements.
• csymamd: For a symmetric positive definite matrix S, returns the permutation vector p such that `S (P, P)' tends to have a sparser Cholesky factor than S.
• dmperm: Perfrom a Deulmage-Mendelsohn permutation on the sparse matrix S.
• etree: Returns the elimination tree for the matrix S.
• etreeplot: Plots the elimination tree of the matrix s or s+s' if s in non-symmetric.
• full: returns a full storage matrix from a sparse one See also: sparse
• gplot: Plots a graph defined by A and xy in the graph theory sense.
• issparse: Return 1 if the value of the expression EXPR is a sparse matrix.
• luinc: Produce the incomplete LU factorization of the sparse A.
• matrix_type: Identify the matrix type or mark a matrix as a particular type.
• nnz: returns number of non zero elements in SM See also: sparse
• nonzeros: Returns a vector of the non-zero values of the sparse matrix S
• nzmax: Returns the amount of storage allocated to the sparse matrix SM.
• spalloc: Returns an empty sparse matrix of size R-by-C.
• sparse: SPARSE: create a sparse matrix
• spatan2: Compute atan (Y / X) for corresponding sparse matrix elements of Y and X.
• spchol: Compute the Cholesky factor, R, of the symmetric positive definite.
• spcholinv: Use the Cholesky factorization to compute the inverse of the sparse symmetric positive definite matrix A.
• spchol2inv: Invert a sparse symmetric, positive definite square matrix from its Cholesky decomposition, U.
• spconvert: This function converts for a simple sparse matrix format easily produced by other programs into Octave's internal sparse format.
• spcumprod: Cumulative product of elements along dimension DIM.
• spcumsum: Cumulative sum of elements along dimension DIM.
• spdet: Compute the determinant of sparse matrix A using UMFPACK.
• spdiag: Return a diagonal matrix with the sparse vector V on diagonal K.
• spdiags: A generalization of the function `spdiag'.
• speye: Returns a sparse identity matrix.
• spfind: SPFIND: a sparse version of the find operator 1.
• spfun: Compute `f(X)' for the non-zero values of X This results in a sparse matrix with the same structure as X.
• spinv: Compute the inverse of the square matrix A.
• spkron: Form the kronecker product of two sparse matrices.
• splchol: Compute the Cholesky factor, L, of the symmetric positive definite.
• splu: Compute the LU decomposition of the sparse matrix A, using subroutines from UMFPACK.
• spmax: For a vector argument, return the maximum value.
• spmin: For a vector argument, return the minimum value.
• spones: Replace the non-zero entries of X with ones.
• spparms: Sets or displays the parameters used by the sparse solvers and factorization functions.
• spprod: Product of elements along dimension DIM.
• sprand: Generate a random sparse matrix.
• sprandn: Generate a random sparse matrix.
• spstats: Return the stats for the non-zero elements of the sparse matrix S COUNT is the number of non-zeros in each column, MEAN is the mean of the non-zeros in each column, and VAR is the variance of the non-zeros in each column
• spsum: Sum of elements along dimension DIM.
• spsumsq: Sum of squares of elements along dimension DIM.
• spy: Plot the sparsity pattern of the sparse matrix X
• symamd: For a symmetric positive definite matrix S, returns the permutation vector p such that `S (P, P)' tends to have a sparser Cholesky factor than S.
• symbfact: Performs a symbolic factorization analysis on the sparse matrix S.
• symrcm: Returns the Reverse Cuthill McKee reordering of the sparse matrix S.
• treeplot: Produces a graph of a tree or forest.