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Matrix data structures, linear solvers, least squares methods, eigenvalue, and singular value decompositions. For larger random dense matrices (above ~ 350 x 350) matrix-matrix multiplication C = A.B is about 50% faster than MTJ.

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package no.uib.cipr.matrix;

import java.util.Arrays;

/**
 * Wraps a DenseVector, allowing easy access to a sub array of the original
 * without taking copies.
 * 
 * It should be possible to utilise BLAS / LAPACK in various matrix classes.
 * However, as it would be a mammoth task, it will be done on an as-needed
 * basis.
 *
 * @author Sam Halliday
 */
public class DenseVectorSub extends AbstractVector {

	private static final long serialVersionUID = -4686967189822912723L;

	private DenseVector wrapped;
    private int offset;

    public DenseVectorSub(DenseVector wrapped, int offset, int size) {
        super(size);
        if (offset + size > wrapped.size)
            throw new IllegalArgumentException(offset + "+" + size + ">"
                    + wrapped.size);
        this.offset = offset;
        this.wrapped = wrapped;
    }

    @Override
    public double get(int index) {
        check(index);
        return wrapped.get(offset + index);
    }

    @Override
    public void set(int index, double value) {
        check(index);
        wrapped.set(offset + index, value);
    }

    @Override
    public DenseVector copy() {
        double[] data = Arrays.copyOfRange(wrapped.getData(), offset, offset
                + size);
        return new DenseVector(data, false);
    }
}