Cholesky.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2016 Kunal Arora
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
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 *    list of conditions and the following disclaimer.
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#ifndef CHOLESKY_IMPL_H_
#define CHOLESKY_IMPL_H_

#include <shogun/lib/config.h>
#include <shogun/lib/SGVector.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/mathematics/eigen3.h>
#include <numeric>

namespace shogun
{

namespace linalg
{

namespace implementation
{

template <enum Backend, class Matrix>
struct cholesky
{

    typedef typename Matrix::Scalar T;

    typedef SGMatrix<T> ReturnType;

    static ReturnType compute(Matrix A, bool lower);

};

template <class Matrix>
struct cholesky<Backend::EIGEN3, Matrix>
{
    typedef typename Matrix::Scalar T;

    typedef SGMatrix<T> ReturnType;

    typedef Eigen::Matrix<T,Eigen::Dynamic,Eigen::Dynamic> MatrixXt;

    static ReturnType compute(SGMatrix<T> A, bool lower)
    {
        //creating eigen3 dense matrices
        Eigen::Map<MatrixXt> map_A(A.matrix, A.num_rows, A.num_cols);

        ReturnType cho(A.num_rows,A.num_cols);
        cho.set_const(0.0);
        Eigen::Map<MatrixXt> map_cho(cho.matrix, cho.num_rows, cho.num_cols);

        Eigen::LLT<MatrixXt> llt(map_A);

        //compute matrix L or U
        if(lower==false)
            map_cho= llt.matrixU();
        else
            map_cho= llt.matrixL();

        // checking for success
        REQUIRE(llt.info()!=Eigen::NumericalIssue, "Matrix is not Hermitian positive definite!\n");

        return cho;
    }

};

}

}

}
#endif //CHOLESKY_IMPL_H_