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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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package org.apache.commons.math.linear;
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* Interface handling decomposition algorithms that can solve A × X = B.
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* <p>Decomposition algorithms decompose an A matrix has a product of several specific
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* matrices from which they can solve A × X = B in least squares sense: they find X
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* such that ||A × X - B|| is minimal.</p>
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* <p>Some solvers like {@link LUDecomposition} can only find the solution for
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* square matrices and when the solution is an exact linear solution, i.e. when
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* ||A × X - B|| is exactly 0. Other solvers can also find solutions
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* with non-square matrix A and with non-null minimal norm. If an exact linear
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* solution exists it is also the minimal norm solution.</p>
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* @version $Revision: 799857 $ $Date: 2009-08-01 09:07:12 -0400 (Sat, 01 Aug 2009) $
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public interface DecompositionSolver {
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/** Solve the linear equation A × X = B for matrices A.
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* <p>The A matrix is implicit, it is provided by the underlying
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* decomposition algorithm.</p>
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* @param b right-hand side of the equation A × X = B
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* @return a vector X that minimizes the two norm of A × X - B
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* @exception IllegalArgumentException if matrices dimensions don't match
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* @exception InvalidMatrixException if decomposed matrix is singular
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double[] solve(final double[] b)
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throws IllegalArgumentException, InvalidMatrixException;
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/** Solve the linear equation A × X = B for matrices A.
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* <p>The A matrix is implicit, it is provided by the underlying
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* decomposition algorithm.</p>
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* @param b right-hand side of the equation A × X = B
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* @return a vector X that minimizes the two norm of A × X - B
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* @exception IllegalArgumentException if matrices dimensions don't match
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* @exception InvalidMatrixException if decomposed matrix is singular
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RealVector solve(final RealVector b)
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throws IllegalArgumentException, InvalidMatrixException;
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/** Solve the linear equation A × X = B for matrices A.
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* <p>The A matrix is implicit, it is provided by the underlying
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* decomposition algorithm.</p>
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* @param b right-hand side of the equation A × X = B
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* @return a matrix X that minimizes the two norm of A × X - B
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* @exception IllegalArgumentException if matrices dimensions don't match
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* @exception InvalidMatrixException if decomposed matrix is singular
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RealMatrix solve(final RealMatrix b)
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throws IllegalArgumentException, InvalidMatrixException;
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* Check if the decomposed matrix is non-singular.
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* @return true if the decomposed matrix is non-singular
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boolean isNonSingular();
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/** Get the inverse (or pseudo-inverse) of the decomposed matrix.
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* @return inverse matrix
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* @throws InvalidMatrixException if decomposed matrix is singular
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RealMatrix getInverse()
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throws InvalidMatrixException;