代码搜索：Definite
找到约 349 项符合「Definite」的源代码
代码结果 349
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h jama_cholesky.h
#ifndef JAMA_CHOLESKY_H
#define JAMA_CHOLESKY_H
#include "math.h"
/* needed for sqrt() below. */
namespace JAMA
{
using namespace TNT;
/**
For a symmetric, positive definite matrix A,
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m geond.m
function p = geond(S,Sxy,minsep,ntries);
% GEOND : Geometric nested dissection ordering.
%
% p = geond(S,Sxy,minsep,ntries). Nested dissection ordering of S.
% For a symmetric positive definite matri
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m spdinv.m
function Minv = SPDinv(M);
%
% Grewal & Andrews, Kalman Filtering Theory and Practice Using MATLAB, 3rd
% Edition, Wiley, 2008.
%
% Inverts symmetric positive definite matrix M using modified Cho
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m spdinvip.m
function Minv = SPDinvIP(M);
%
% Grewal & Andrews, Kalman Filtering Theory and Practice Using MATLAB, 3rd
% Edition, Wiley, 2008.
%
% Inverts symmetric positive definite matrix M using modified C
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mtx help.mtx
func chol
chol (X)
Returns the cholesky decomposition, B, of X, such that B*B' = X. B
will be lower triangular. X must be symmetric and positive definite.
func cols
cols (X)
Returns
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m alg066.m
% CHOLESKI'S ALGORITHM 6.6
%
% To factor the positive definite n by n matrix A into LL**T,
% where L is lower triangular.
%
% INPUT: the dimension n; entries A(I,J), 1
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m alg066.m
% CHOLESKI'S ALGORITHM 6.6
%
% To factor the positive definite n by n matrix A into LL**T,
% where L is lower triangular.
%
% INPUT: the dimension n; entries A(I,J), 1
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m randpds.m
function [C]=randpds(dim,diagm)
% [C]=randpds(dim,diagm)
%
% RANDPDS generates random positive definite symetric matrix of
% given dimension.
%
% Input:
% dim [1x1] given dimension of desired matri
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m randpds.m
function [C]=randpds(dim,diagm)
% [C]=randpds(dim,diagm)
%
% RANDPDS generates random positive definite symetric matrix of
% given dimension.
%
% Input:
% dim [1x1] given dimension of desired matri
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m alg066.m
% CHOLESKI'S ALGORITHM 6.6
%
% To factor the positive definite n by n matrix A into LL**T,
% where L is lower triangular.
%
% INPUT: the dimension n; entries A(I,J), 1