#ifndef Magnum_Math_Algorithms_GaussJordan_h
#define Magnum_Math_Algorithms_GaussJordan_h
/*
    This file is part of Magnum.

    Copyright © 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019
              Vladimír Vondruš <mosra@centrum.cz>

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    Software is furnished to do so, subject to the following conditions:

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*/

/** @file
 * @brief Function @ref Magnum::Math::Algorithms::gaussJordanInPlaceTransposed(), @ref Magnum::Math::Algorithms::gaussJordanInPlace(), @ref Magnum::Math::Algorithms::gaussJordanInverted()
 */

#include "Magnum/Math/Matrix.h"

namespace Magnum { namespace Math { namespace Algorithms {

/**
@brief In-place Gauss-Jordan elimination on transposed matrices
@param a     Transposed left side of augmented matrix
@param t     Transposed right side of augmented matrix
@return True if @p a is regular, false if @p a is singular (and thus the
    system cannot be solved).

As [Gauss-Jordan elimination](https://en.wikipedia.org/wiki/Gaussian_elimination)
works on rows and matrices in OpenGL are column-major, it is more efficient to
operate on transposed matrices and treat columns as rows. See also
@ref gaussJordanInPlace() which works with non-transposed matrices.

The function eliminates matrix @p a and solves @p t in place. For efficiency
reasons, only pure Gaussian elimination is done on @p a and the final
backsubstitution is done only on @p t, as @p a would always end with identity
matrix anyway.

Based on an ultra-compact Python code by Jarno Elonen,
http://elonen.iki.fi/code/misc-notes/python-gaussj/index.html.
*/
template<std::size_t size, std::size_t rows, class T> bool gaussJordanInPlaceTransposed(RectangularMatrix<size, size, T>& a, RectangularMatrix<size, rows, T>& t) {
    for(std::size_t row = 0; row != size; ++row) {
        /* Find max pivot */
        std::size_t rowMax = row;
        for(std::size_t row2 = row+1; row2 != size; ++row2)
            if(std::abs(a[row2][row]) > std::abs(a[rowMax][row]))
                rowMax = row2;

        /* Swap the rows */
        using std::swap;
        swap(a[row], a[rowMax]);
        swap(t[row], t[rowMax]);

        /* Singular */
        if(TypeTraits<T>::equals(a[row][row], T(0)))
            return false;

        /* Eliminate column */
        for(std::size_t row2 = row+1; row2 != size; ++row2) {
            T c = a[row2][row]/a[row][row];

            a[row2] -= a[row]*c;
            t[row2] -= t[row]*c;
        }
    }

    /* Backsubstitute */
    for(std::size_t row = size; row != 0; --row) {
        T c = T(1)/a[row-1][row-1];

        for(std::size_t row2 = 0; row2 != row-1; ++row2)
            t[row2] -= t[row-1]*a[row2][row-1]*c;

        /* Normalize the row */
        t[row-1] *= c;
    }

    return true;
}

/**
@brief In-place Gauss-Jordan elimination

Transposes the matrices, calls @ref gaussJordanInPlaceTransposed() on them and
then transposes them back.
*/
template<std::size_t size, std::size_t cols, class T> bool gaussJordanInPlace(RectangularMatrix<size, size, T>& a, RectangularMatrix<cols, size, T>& t) {
    a = a.transposed();
    RectangularMatrix<size, cols, T> tTransposed = t.transposed();

    bool ret = gaussJordanInPlaceTransposed(a, tTransposed);

    a = a.transposed();
    t = tTransposed.transposed();

    return ret;
}

/**
@brief Gauss-Jordan matrix inversion

Uses the [Gauss-Jordan elimination](https://en.wikipedia.org/wiki/Gaussian_elimination#Finding_the_inverse_of_a_matrix) to perform a matrix
inversion. Since @f$ (\boldsymbol{A}^{-1})^T = (\boldsymbol{A}^T)^{-1} @f$,
passes @p matrix and an identity matrix to @ref gaussJordanInPlaceTransposed();
returning the inverted matrix. Expects that the matrix is invertible.
@see @ref Matrix::inverted()
*/
template<std::size_t size, class T> Matrix<size, T> gaussJordanInverted(Matrix<size, T> matrix) {
    Matrix<size, T> inverted{Math::IdentityInit};
    CORRADE_INTERNAL_ASSERT_OUTPUT(gaussJordanInPlaceTransposed(matrix, inverted));
    return inverted;
}

}}}

#endif
