Java inverse matrix calculation

Question

I\'m trying to calculate the inverse matrix in Java.

I\'m following the adjoint method (first calculation of the adjoint matrix, then transpose this matrix and fina

Answer 1

By ACM you can do this without rhs:

RealMatrix A = new Array2DRowRealMatrix(ARR);
System.out.println(new LUDecomposition(A).getSolver().getInverse());

Answer 2

The la4j (Linear Algebra for Java) library supports matrix inversion. Here is the brief example:

Matrix a = new Basic2DMatrix(new double[][]{
   { 1.0, 2.0, 3.0 },
   { 4.0, 5.0, 6.0 },
   { 7.0, 8.0. 9.0 }
});

Matrix b = a.invert(Matrices.DEFAULT_INVERTOR); // uses Gaussian Elimination

Answer 3

For those who seeks matrix inversion (not fast), see https://github.com/rchen8/Algorithms/blob/master/Matrix.java.

import java.util.Arrays;

public class Matrix {

    private static double determinant(double[][] matrix) {
        if (matrix.length != matrix[0].length)
            throw new IllegalStateException("invalid dimensions");

        if (matrix.length == 2)
            return matrix[0][0] * matrix[1][1] - matrix[0][1] * matrix[1][0];

        double det = 0;
        for (int i = 0; i < matrix[0].length; i++)
            det += Math.pow(-1, i) * matrix[0][i]
                    * determinant(minor(matrix, 0, i));
        return det;
    }

    private static double[][] inverse(double[][] matrix) {
        double[][] inverse = new double[matrix.length][matrix.length];

        // minors and cofactors
        for (int i = 0; i < matrix.length; i++)
            for (int j = 0; j < matrix[i].length; j++)
                inverse[i][j] = Math.pow(-1, i + j)
                        * determinant(minor(matrix, i, j));

        // adjugate and determinant
        double det = 1.0 / determinant(matrix);
        for (int i = 0; i < inverse.length; i++) {
            for (int j = 0; j <= i; j++) {
                double temp = inverse[i][j];
                inverse[i][j] = inverse[j][i] * det;
                inverse[j][i] = temp * det;
            }
        }

        return inverse;
    }

    private static double[][] minor(double[][] matrix, int row, int column) {
        double[][] minor = new double[matrix.length - 1][matrix.length - 1];

        for (int i = 0; i < matrix.length; i++)
            for (int j = 0; i != row && j < matrix[i].length; j++)
                if (j != column)
                    minor[i < row ? i : i - 1][j < column ? j : j - 1] = matrix[i][j];
        return minor;
    }

    private static double[][] multiply(double[][] a, double[][] b) {
        if (a[0].length != b.length)
            throw new IllegalStateException("invalid dimensions");

        double[][] matrix = new double[a.length][b[0].length];
        for (int i = 0; i < a.length; i++) {
            for (int j = 0; j < b[0].length; j++) {
                double sum = 0;
                for (int k = 0; k < a[i].length; k++)
                    sum += a[i][k] * b[k][j];
                matrix[i][j] = sum;
            }
        }

        return matrix;
    }

    private static double[][] rref(double[][] matrix) {
        double[][] rref = new double[matrix.length][];
        for (int i = 0; i < matrix.length; i++)
            rref[i] = Arrays.copyOf(matrix[i], matrix[i].length);

        int r = 0;
        for (int c = 0; c < rref[0].length && r < rref.length; c++) {
            int j = r;
            for (int i = r + 1; i < rref.length; i++)
                if (Math.abs(rref[i][c]) > Math.abs(rref[j][c]))
                    j = i;
            if (Math.abs(rref[j][c]) < 0.00001)
                continue;

            double[] temp = rref[j];
            rref[j] = rref[r];
            rref[r] = temp;

            double s = 1.0 / rref[r][c];
            for (j = 0; j < rref[0].length; j++)
                rref[r][j] *= s;
            for (int i = 0; i < rref.length; i++) {
                if (i != r) {
                    double t = rref[i][c];
                    for (j = 0; j < rref[0].length; j++)
                        rref[i][j] -= t * rref[r][j];
                }
            }
            r++;
        }

        return rref;
    }

    private static double[][] transpose(double[][] matrix) {
        double[][] transpose = new double[matrix[0].length][matrix.length];

        for (int i = 0; i < matrix.length; i++)
            for (int j = 0; j < matrix[i].length; j++)
                transpose[j][i] = matrix[i][j];
        return transpose;
    }

    public static void main(String[] args) {
        // example 1 - solving a system of equations
        double[][] a = { { 1, 1, 1 }, { 0, 2, 5 }, { 2, 5, -1 } };
        double[][] b = { { 6 }, { -4 }, { 27 } };

        double[][] matrix = multiply(inverse(a), b);
        for (double[] i : matrix)
            System.out.println(Arrays.toString(i));
        System.out.println();

        // example 2 - example 1 using reduced row echelon form
        a = new double[][]{ { 1, 1, 1, 6 }, { 0, 2, 5, -4 }, { 2, 5, -1, 27 } };
        matrix = rref(a);
        for (double[] i : matrix)
            System.out.println(Arrays.toString(i));
        System.out.println();

        // example 3 - solving a normal equation for linear regression
        double[][] x = { { 2104, 5, 1, 45 }, { 1416, 3, 2, 40 },
                { 1534, 3, 2, 30 }, { 852, 2, 1, 36 } };
        double[][] y = { { 460 }, { 232 }, { 315 }, { 178 } };

        matrix = multiply(
                multiply(inverse(multiply(transpose(x), x)), transpose(x)), y);
        for (double[] i : matrix)
            System.out.println(Arrays.toString(i));
    }

}

Answer 4

It is tough to beat Matlab at their game. They also are cautiou about precision. If you have 2.0 and 2.00001 as pivots - watch out! Your answer might end up being very imprecise. Also, check out Python's implementation (it is in numpy / scipy somewhere ...)

Answer 5

Do you have to have an exact solution? An approximate solver (Gauss-Seidel is very performant and easy to implement) will likely work for you, and will converge very quickly. 19x19 is quite a small matrix. I think the Gauss-Seidel code that I used could solve a 128x128 matrix in the blink of an eye (but don't quote me on that, it's been a while).