1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 package org.apache.commons.math.linear; 19 20 21 22 /** 23 * Interface handling decomposition algorithms that can solve A × X = B. 24 * <p>Decomposition algorithms decompose an A matrix has a product of several specific 25 * matrices from which they can solve A × X = B in least squares sense: they find X 26 * such that ||A × X - B|| is minimal.</p> 27 * <p>Some solvers like {@link LUDecomposition} can only find the solution for 28 * square matrices and when the solution is an exact linear solution, i.e. when 29 * ||A × X - B|| is exactly 0. Other solvers can also find solutions 30 * with non-square matrix A and with non-null minimal norm. If an exact linear 31 * solution exists it is also the minimal norm solution.</p> 32 * 33 * @version $Revision: 811685 $ $Date: 2009-09-05 19:36:48 +0200 (sam. 05 sept. 2009) $ 34 * @since 2.0 35 */ 36 public interface DecompositionSolver { 37 38 /** Solve the linear equation A × X = B for matrices A. 39 * <p>The A matrix is implicit, it is provided by the underlying 40 * decomposition algorithm.</p> 41 * @param b right-hand side of the equation A × X = B 42 * @return a vector X that minimizes the two norm of A × X - B 43 * @exception IllegalArgumentException if matrices dimensions don't match 44 * @exception InvalidMatrixException if decomposed matrix is singular 45 */ 46 double[] solve(final double[] b) 47 throws IllegalArgumentException, InvalidMatrixException; 48 49 /** Solve the linear equation A × X = B for matrices A. 50 * <p>The A matrix is implicit, it is provided by the underlying 51 * decomposition algorithm.</p> 52 * @param b right-hand side of the equation A × X = B 53 * @return a vector X that minimizes the two norm of A × X - B 54 * @exception IllegalArgumentException if matrices dimensions don't match 55 * @exception InvalidMatrixException if decomposed matrix is singular 56 */ 57 RealVector solve(final RealVector b) 58 throws IllegalArgumentException, InvalidMatrixException; 59 60 /** Solve the linear equation A × X = B for matrices A. 61 * <p>The A matrix is implicit, it is provided by the underlying 62 * decomposition algorithm.</p> 63 * @param b right-hand side of the equation A × X = B 64 * @return a matrix X that minimizes the two norm of A × X - B 65 * @exception IllegalArgumentException if matrices dimensions don't match 66 * @exception InvalidMatrixException if decomposed matrix is singular 67 */ 68 RealMatrix solve(final RealMatrix b) 69 throws IllegalArgumentException, InvalidMatrixException; 70 71 /** 72 * Check if the decomposed matrix is non-singular. 73 * @return true if the decomposed matrix is non-singular 74 */ 75 boolean isNonSingular(); 76 77 /** Get the inverse (or pseudo-inverse) of the decomposed matrix. 78 * @return inverse matrix 79 * @throws InvalidMatrixException if decomposed matrix is singular 80 */ 81 RealMatrix getInverse() 82 throws InvalidMatrixException; 83 84 } 85
