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Java example source code file (FieldLUDecomposition.java)

This example Java source code file (FieldLUDecomposition.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

array2drowfieldmatrix, arrayfieldvector, classcastexception, dimensionmismatchexception, fielddecompositionsolver, fieldelement, fieldludecomposition, fieldmatrix, singularmatrixexception, solver

The FieldLUDecomposition.java Java example source code

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.commons.math3.linear;

import org.apache.commons.math3.Field;
import org.apache.commons.math3.FieldElement;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.util.MathArrays;

/**
 * Calculates the LUP-decomposition of a square matrix.
 * <p>The LUP-decomposition of a matrix A consists of three matrices
 * L, U and P that satisfy: PA = LU, L is lower triangular, and U is
 * upper triangular and P is a permutation matrix. All matrices are
 * m×m.</p>
 * <p>Since {@link FieldElement field elements} do not provide an ordering
 * operator, the permutation matrix is computed here only in order to avoid
 * a zero pivot element, no attempt is done to get the largest pivot
 * element.</p>
 * <p>This class is based on the class with similar name from the
 * <a href="http://math.nist.gov/javanumerics/jama/">JAMA library.

* <ul> * <li>a {@link #getP() getP} method has been added, * <li>the {@code det} method has been renamed as {@link #getDeterminant() * getDeterminant},</li> * <li>the {@code getDoublePivot} method has been removed (but the int based * {@link #getPivot() getPivot} method has been kept),</li> * <li>the {@code solve} and {@code isNonSingular} methods have been replaced * by a {@link #getSolver() getSolver} method and the equivalent methods * provided by the returned {@link DecompositionSolver}.</li> * </ul> * * @param <T> the type of the field elements * @see <a href="http://mathworld.wolfram.com/LUDecomposition.html">MathWorld * @see <a href="http://en.wikipedia.org/wiki/LU_decomposition">Wikipedia * @since 2.0 (changed to concrete class in 3.0) */ public class FieldLUDecomposition<T extends FieldElement { /** Field to which the elements belong. */ private final Field<T> field; /** Entries of LU decomposition. */ private T[][] lu; /** Pivot permutation associated with LU decomposition. */ private int[] pivot; /** Parity of the permutation associated with the LU decomposition. */ private boolean even; /** Singularity indicator. */ private boolean singular; /** Cached value of L. */ private FieldMatrix<T> cachedL; /** Cached value of U. */ private FieldMatrix<T> cachedU; /** Cached value of P. */ private FieldMatrix<T> cachedP; /** * Calculates the LU-decomposition of the given matrix. * @param matrix The matrix to decompose. * @throws NonSquareMatrixException if matrix is not square */ public FieldLUDecomposition(FieldMatrix<T> matrix) { if (!matrix.isSquare()) { throw new NonSquareMatrixException(matrix.getRowDimension(), matrix.getColumnDimension()); } final int m = matrix.getColumnDimension(); field = matrix.getField(); lu = matrix.getData(); pivot = new int[m]; cachedL = null; cachedU = null; cachedP = null; // Initialize permutation array and parity for (int row = 0; row < m; row++) { pivot[row] = row; } even = true; singular = false; // Loop over columns for (int col = 0; col < m; col++) { T sum = field.getZero(); // upper for (int row = 0; row < col; row++) { final T[] luRow = lu[row]; sum = luRow[col]; for (int i = 0; i < row; i++) { sum = sum.subtract(luRow[i].multiply(lu[i][col])); } luRow[col] = sum; } // lower int nonZero = col; // permutation row for (int row = col; row < m; row++) { final T[] luRow = lu[row]; sum = luRow[col]; for (int i = 0; i < col; i++) { sum = sum.subtract(luRow[i].multiply(lu[i][col])); } luRow[col] = sum; if (lu[nonZero][col].equals(field.getZero())) { // try to select a better permutation choice ++nonZero; } } // Singularity check if (nonZero >= m) { singular = true; return; } // Pivot if necessary if (nonZero != col) { T tmp = field.getZero(); for (int i = 0; i < m; i++) { tmp = lu[nonZero][i]; lu[nonZero][i] = lu[col][i]; lu[col][i] = tmp; } int temp = pivot[nonZero]; pivot[nonZero] = pivot[col]; pivot[col] = temp; even = !even; } // Divide the lower elements by the "winning" diagonal elt. final T luDiag = lu[col][col]; for (int row = col + 1; row < m; row++) { final T[] luRow = lu[row]; luRow[col] = luRow[col].divide(luDiag); } } } /** * Returns the matrix L of the decomposition. * <p>L is a lower-triangular matrix

* @return the L matrix (or null if decomposed matrix is singular) */ public FieldMatrix<T> getL() { if ((cachedL == null) && !singular) { final int m = pivot.length; cachedL = new Array2DRowFieldMatrix<T>(field, m, m); for (int i = 0; i < m; ++i) { final T[] luI = lu[i]; for (int j = 0; j < i; ++j) { cachedL.setEntry(i, j, luI[j]); } cachedL.setEntry(i, i, field.getOne()); } } return cachedL; } /** * Returns the matrix U of the decomposition. * <p>U is an upper-triangular matrix

* @return the U matrix (or null if decomposed matrix is singular) */ public FieldMatrix<T> getU() { if ((cachedU == null) && !singular) { final int m = pivot.length; cachedU = new Array2DRowFieldMatrix<T>(field, m, m); for (int i = 0; i < m; ++i) { final T[] luI = lu[i]; for (int j = i; j < m; ++j) { cachedU.setEntry(i, j, luI[j]); } } } return cachedU; } /** * Returns the P rows permutation matrix. * <p>P is a sparse matrix with exactly one element set to 1.0 in * each row and each column, all other elements being set to 0.0.</p> * <p>The positions of the 1 elements are given by the {@link #getPivot() * pivot permutation vector}.</p> * @return the P rows permutation matrix (or null if decomposed matrix is singular) * @see #getPivot() */ public FieldMatrix<T> getP() { if ((cachedP == null) && !singular) { final int m = pivot.length; cachedP = new Array2DRowFieldMatrix<T>(field, m, m); for (int i = 0; i < m; ++i) { cachedP.setEntry(i, pivot[i], field.getOne()); } } return cachedP; } /** * Returns the pivot permutation vector. * @return the pivot permutation vector * @see #getP() */ public int[] getPivot() { return pivot.clone(); } /** * Return the determinant of the matrix. * @return determinant of the matrix */ public T getDeterminant() { if (singular) { return field.getZero(); } else { final int m = pivot.length; T determinant = even ? field.getOne() : field.getZero().subtract(field.getOne()); for (int i = 0; i < m; i++) { determinant = determinant.multiply(lu[i][i]); } return determinant; } } /** * Get a solver for finding the A × X = B solution in exact linear sense. * @return a solver */ public FieldDecompositionSolver<T> getSolver() { return new Solver<T>(field, lu, pivot, singular); } /** Specialized solver. * @param <T> the type of the field elements */ private static class Solver<T extends FieldElement implements FieldDecompositionSolver { /** Field to which the elements belong. */ private final Field<T> field; /** Entries of LU decomposition. */ private final T[][] lu; /** Pivot permutation associated with LU decomposition. */ private final int[] pivot; /** Singularity indicator. */ private final boolean singular; /** * Build a solver from decomposed matrix. * @param field field to which the matrix elements belong * @param lu entries of LU decomposition * @param pivot pivot permutation associated with LU decomposition * @param singular singularity indicator */ private Solver(final Field<T> field, final T[][] lu, final int[] pivot, final boolean singular) { this.field = field; this.lu = lu; this.pivot = pivot; this.singular = singular; } /** {@inheritDoc} */ public boolean isNonSingular() { return !singular; } /** {@inheritDoc} */ public FieldVector<T> solve(FieldVector b) { try { return solve((ArrayFieldVector<T>) b); } catch (ClassCastException cce) { final int m = pivot.length; if (b.getDimension() != m) { throw new DimensionMismatchException(b.getDimension(), m); } if (singular) { throw new SingularMatrixException(); } // Apply permutations to b final T[] bp = MathArrays.buildArray(field, m); for (int row = 0; row < m; row++) { bp[row] = b.getEntry(pivot[row]); } // Solve LY = b for (int col = 0; col < m; col++) { final T bpCol = bp[col]; for (int i = col + 1; i < m; i++) { bp[i] = bp[i].subtract(bpCol.multiply(lu[i][col])); } } // Solve UX = Y for (int col = m - 1; col >= 0; col--) { bp[col] = bp[col].divide(lu[col][col]); final T bpCol = bp[col]; for (int i = 0; i < col; i++) { bp[i] = bp[i].subtract(bpCol.multiply(lu[i][col])); } } return new ArrayFieldVector<T>(field, bp, false); } } /** Solve the linear equation A × X = B. * <p>The A matrix is implicit here. It is

* @param b right-hand side of the equation A × X = B * @return a vector X such that A × X = B * @throws DimensionMismatchException if the matrices dimensions do not match. * @throws SingularMatrixException if the decomposed matrix is singular. */ public ArrayFieldVector<T> solve(ArrayFieldVector b) { final int m = pivot.length; final int length = b.getDimension(); if (length != m) { throw new DimensionMismatchException(length, m); } if (singular) { throw new SingularMatrixException(); } // Apply permutations to b final T[] bp = MathArrays.buildArray(field, m); for (int row = 0; row < m; row++) { bp[row] = b.getEntry(pivot[row]); } // Solve LY = b for (int col = 0; col < m; col++) { final T bpCol = bp[col]; for (int i = col + 1; i < m; i++) { bp[i] = bp[i].subtract(bpCol.multiply(lu[i][col])); } } // Solve UX = Y for (int col = m - 1; col >= 0; col--) { bp[col] = bp[col].divide(lu[col][col]); final T bpCol = bp[col]; for (int i = 0; i < col; i++) { bp[i] = bp[i].subtract(bpCol.multiply(lu[i][col])); } } return new ArrayFieldVector<T>(bp, false); } /** {@inheritDoc} */ public FieldMatrix<T> solve(FieldMatrix b) { final int m = pivot.length; if (b.getRowDimension() != m) { throw new DimensionMismatchException(b.getRowDimension(), m); } if (singular) { throw new SingularMatrixException(); } final int nColB = b.getColumnDimension(); // Apply permutations to b final T[][] bp = MathArrays.buildArray(field, m, nColB); for (int row = 0; row < m; row++) { final T[] bpRow = bp[row]; final int pRow = pivot[row]; for (int col = 0; col < nColB; col++) { bpRow[col] = b.getEntry(pRow, col); } } // Solve LY = b for (int col = 0; col < m; col++) { final T[] bpCol = bp[col]; for (int i = col + 1; i < m; i++) { final T[] bpI = bp[i]; final T luICol = lu[i][col]; for (int j = 0; j < nColB; j++) { bpI[j] = bpI[j].subtract(bpCol[j].multiply(luICol)); } } } // Solve UX = Y for (int col = m - 1; col >= 0; col--) { final T[] bpCol = bp[col]; final T luDiag = lu[col][col]; for (int j = 0; j < nColB; j++) { bpCol[j] = bpCol[j].divide(luDiag); } for (int i = 0; i < col; i++) { final T[] bpI = bp[i]; final T luICol = lu[i][col]; for (int j = 0; j < nColB; j++) { bpI[j] = bpI[j].subtract(bpCol[j].multiply(luICol)); } } } return new Array2DRowFieldMatrix<T>(field, bp, false); } /** {@inheritDoc} */ public FieldMatrix<T> getInverse() { final int m = pivot.length; final T one = field.getOne(); FieldMatrix<T> identity = new Array2DRowFieldMatrix(field, m, m); for (int i = 0; i < m; ++i) { identity.setEntry(i, i, one); } return solve(identity); } } }

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