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A fast and easy to use dense and sparse matrix linear algebra library written in Java.
/*
* Copyright (c) 2022, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed 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.ejml.dense.row.linsol;
import javax.annotation.Generated;
import org.ejml.data.FMatrix1Row;
import org.ejml.data.FMatrixRMaj;
import org.ejml.dense.row.CommonOps_FDRM;
import org.ejml.interfaces.linsol.LinearSolverDense;
/**
* A matrix can be easily inverted by solving a system with an identify matrix. The only
* disadvantage of this approach is that additional computations are required compared to
* a specialized solution.
*
* @author Peter Abeles
*/
@Generated("org.ejml.dense.row.linsol.InvertUsingSolve_DDRM")
public class InvertUsingSolve_FDRM {
public static void invert( LinearSolverDense solver, FMatrix1Row A, FMatrixRMaj A_inv, FMatrixRMaj storage ) {
if (A.numRows != A_inv.numRows || A.numCols != A_inv.numCols) {
throw new IllegalArgumentException("A and A_inv must have the same dimensions");
}
CommonOps_FDRM.setIdentity(storage);
solver.solve(storage, A_inv);
}
public static void invert( LinearSolverDense solver, FMatrix1Row A, FMatrixRMaj A_inv ) {
if (A.numRows != A_inv.numRows || A.numCols != A_inv.numCols) {
throw new IllegalArgumentException("A and A_inv must have the same dimensions");
}
CommonOps_FDRM.setIdentity(A_inv);
solver.solve(A_inv, A_inv);
}
}