Example usage for org.apache.commons.math3.linear ArrayRealVector getNorm

Introduction

In this page you can find the example usage for org.apache.commons.math3.linear ArrayRealVector getNorm.

Prototype

@Override
public double getNorm() 

Source Link

Usage

From source file:info.rmarcus.birkhoffvonneumann.MatrixUtils.java

public static double[] normalize(double[] input) {
    ArrayRealVector arv = new ArrayRealVector(input);
    arv.mapDivideToSelf(arv.getNorm());
    return NullUtils.orThrow(arv.toArray(), () -> new BVNRuntimeException("Could not normalize array!"));
}

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From source file:fp.overlapr.algorithmen.StressMajorization.java

@Deprecated
private static ArrayRealVector conjugateGradientsMethod(Array2DRowRealMatrix A, ArrayRealVector b,
        ArrayRealVector werte) {/*  w ww  . j av a  2s. c o m*/

    Array2DRowRealMatrix preJacobi = new Array2DRowRealMatrix(A.getRowDimension(), A.getColumnDimension());

    // Predconditioner berechnen
    preJacobi.walkInRowOrder(new DefaultRealMatrixChangingVisitor() {
        @Override
        public double visit(int row, int column, double value) {
            if (row == column) {
                return 1 / A.getEntry(row, column);
            } else {
                return 0.0;
            }
        }
    });

    // x_k beliebig whlen
    ArrayRealVector x_k = new ArrayRealVector(werte);

    // r_k berechnen
    ArrayRealVector r_k = b.subtract(A.operate(x_k));

    // h_k berechnen
    ArrayRealVector h_k = (ArrayRealVector) preJacobi.operate(r_k);

    // d_k = r_k
    ArrayRealVector d_k = h_k;

    // x_k+1 und r_k+1 und d_k+1, sowie alpha und beta und z erzeugen
    ArrayRealVector x_k1;
    ArrayRealVector r_k1;
    ArrayRealVector d_k1;
    ArrayRealVector h_k1;
    double alpha;
    double beta;
    ArrayRealVector z;

    do {
        // Speichere Matrix-Vektor-Produkt, um es nur einmal auszurechnen
        z = (ArrayRealVector) A.operate(d_k);

        // Finde von x_k in Richtung d_k den Ort x_k1 des Minimums der
        // Funktion E
        // und aktualisere den Gradienten bzw. das Residuum
        alpha = r_k.dotProduct(h_k) / d_k.dotProduct(z);
        x_k1 = x_k.add(d_k.mapMultiply(alpha));
        r_k1 = r_k.subtract(z.mapMultiply(alpha));
        h_k1 = (ArrayRealVector) preJacobi.operate(r_k1);

        // Korrigiere die Suchrichtung d_k1
        beta = r_k1.dotProduct(h_k1) / r_k.dotProduct(h_k);
        d_k1 = h_k1.add(d_k.mapMultiply(beta));

        // Werte "eins" weitersetzen
        x_k = x_k1;
        r_k = r_k1;
        d_k = d_k1;
        h_k = h_k1;

    } while (r_k1.getNorm() > TOL);

    return x_k1;
}

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