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/* * Copyright (c) 2010-2021 Haifeng Li. All rights reserved. * * Smile is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * Smile is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with Smile. If not, see . */ /** * Matrix interface, dense and sparse (band or irregular) matrix encapsulation * classes, LU, QR, Cholesky, SVD and eigen decompositions, etc. A matrix * is a rectangular array of numbers, symbols, or expressions. The individual * items in a matrix are called its elements or entries. *

* One of most important matrix operations is the matrix vector * multiplication, which is the only operation needed in many iterative matrix * algorithms, e.g. biconjugate gradient method for solving linear equations and * power iteration and Lanczos algorithm for eigen decomposition, which are * usually very efficient for very large and sparse matrices. * * @author Haifeng Li */ package smile.math.matrix;