matd.c

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/* Copyright (C) 2013-2016, The Regents of The University of Michigan.
All rights reserved.
This software was developed in the APRIL Robotics Lab under the
direction of Edwin Olson, ebolson@umich.edu. This software may be
available under alternative licensing terms; contact the address above.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
   list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
   this list of conditions and the following disclaimer in the documentation
   and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of the Regents of The University of Michigan.
*/

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdarg.h>
#include <assert.h>
#include <math.h>
#include <float.h>

#include "common/math_util.h"
#include "common/svd22.h"
#include "common/matd.h"
#include "common/debug_print.h"

// a matd_t with rows=0 cols=0 is a SCALAR.

// to ease creating mati, matf, etc. in the future.
#define TYPE double

matd_t *matd_create(int rows, int cols)
{
    assert(rows >= 0);
    assert(cols >= 0);

    if (rows == 0 || cols == 0)
        return matd_create_scalar(0);

    matd_t *m = calloc(1, sizeof(matd_t) + (rows*cols*sizeof(double)));
    m->nrows = rows;
    m->ncols = cols;

    return m;
}

matd_t *matd_create_scalar(TYPE v)
{
    matd_t *m = calloc(1, sizeof(matd_t) + sizeof(double));
    m->nrows = 0;
    m->ncols = 0;
    m->data[0] = v;

    return m;
}

matd_t *matd_create_data(int rows, int cols, const TYPE *data)
{
    if (rows == 0 || cols == 0)
        return matd_create_scalar(data[0]);

    matd_t *m = matd_create(rows, cols);
    for (int i = 0; i < rows * cols; i++)
        m->data[i] = data[i];

    return m;
}

matd_t *matd_create_dataf(int rows, int cols, const float *data)
{
    if (rows == 0 || cols == 0)
        return matd_create_scalar(data[0]);

    matd_t *m = matd_create(rows, cols);
    for (int i = 0; i < rows * cols; i++)
        m->data[i] = (double)data[i];

    return m;
}

matd_t *matd_identity(int dim)
{
    if (dim == 0)
        return matd_create_scalar(1);

    matd_t *m = matd_create(dim, dim);
    for (int i = 0; i < dim; i++)
        MATD_EL(m, i, i) = 1;

    return m;
}

// row and col are zero-based
TYPE matd_get(const matd_t *m, int row, int col)
{
    assert(m != NULL);
    assert(!matd_is_scalar(m));
    assert(row >= 0);
    assert(row < m->nrows);
    assert(col >= 0);
    assert(col < m->ncols);

    return MATD_EL(m, row, col);
}

// row and col are zero-based
void matd_put(matd_t *m, int row, int col, TYPE value)
{
    assert(m != NULL);

    if (matd_is_scalar(m)) {
        matd_put_scalar(m, value);
        return;
    }

    assert(row >= 0);
    assert(row < m->nrows);
    assert(col >= 0);
    assert(col < m->ncols);

    MATD_EL(m, row, col) = value;
}

TYPE matd_get_scalar(const matd_t *m)
{
    assert(m != NULL);
    assert(matd_is_scalar(m));

    return (m->data[0]);
}

void matd_put_scalar(matd_t *m, TYPE value)
{
    assert(m != NULL);
    assert(matd_is_scalar(m));

    m->data[0] = value;
}

matd_t *matd_copy(const matd_t *m)
{
    assert(m != NULL);

    matd_t *x = matd_create(m->nrows, m->ncols);
    if (matd_is_scalar(m))
        x->data[0] = m->data[0];
    else
        memcpy(x->data, m->data, sizeof(TYPE)*m->ncols*m->nrows);

    return x;
}

matd_t *matd_select(const matd_t * a, int r0, int r1, int c0, int c1)
{
    assert(a != NULL);

    assert(r0 >= 0 && r0 < a->nrows);
    assert(c0 >= 0 && c0 < a->ncols);

    int nrows = r1 - r0 + 1;
    int ncols = c1 - c0 + 1;

    matd_t * r = matd_create(nrows, ncols);

    for (int row = r0; row <= r1; row++)
        for (int col = c0; col <= c1; col++)
            MATD_EL(r,row-r0,col-c0) = MATD_EL(a,row,col);

    return r;
}

void matd_print(const matd_t *m, const char *fmt)
{
    assert(m != NULL);
    assert(fmt != NULL);

    if (matd_is_scalar(m)) {
        printf(fmt, MATD_EL(m, 0, 0));
        printf("\n");
    } else {
        for (int i = 0; i < m->nrows; i++) {
            for (int j = 0; j < m->ncols; j++) {
                printf(fmt, MATD_EL(m, i, j));
            }
            printf("\n");
        }
    }
}

void matd_print_transpose(const matd_t *m, const char *fmt)
{
    assert(m != NULL);
    assert(fmt != NULL);

    if (matd_is_scalar(m)) {
        printf(fmt, MATD_EL(m, 0, 0));
        printf("\n");
    } else {
        for (int j = 0; j < m->ncols; j++) {
            for (int i = 0; i < m->nrows; i++) {
                printf(fmt, MATD_EL(m, i, j));
            }
            printf("\n");
        }
    }
}

void matd_destroy(matd_t *m)
{
    if (!m)
        return;

    assert(m != NULL);
    free(m);
}

matd_t *matd_multiply(const matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);

    if (matd_is_scalar(a))
        return matd_scale(b, a->data[0]);
    if (matd_is_scalar(b))
        return matd_scale(a, b->data[0]);

    assert(a->ncols == b->nrows);
    matd_t *m = matd_create(a->nrows, b->ncols);

    for (int i = 0; i < m->nrows; i++) {
        for (int j = 0; j < m->ncols; j++) {
            TYPE acc = 0;
            for (int k = 0; k < a->ncols; k++) {
                acc += MATD_EL(a, i, k) * MATD_EL(b, k, j);
            }
            MATD_EL(m, i, j) = acc;
        }
    }

    return m;
}

matd_t *matd_scale(const matd_t *a, double s)
{
    assert(a != NULL);

    if (matd_is_scalar(a))
        return matd_create_scalar(a->data[0] * s);

    matd_t *m = matd_create(a->nrows, a->ncols);

    for (int i = 0; i < m->nrows; i++) {
        for (int j = 0; j < m->ncols; j++) {
            MATD_EL(m, i, j) = s * MATD_EL(a, i, j);
        }
    }

    return m;
}

void matd_scale_inplace(matd_t *a, double s)
{
    assert(a != NULL);

    if (matd_is_scalar(a)) {
        a->data[0] *= s;
        return;
    }

    for (int i = 0; i < a->nrows; i++) {
        for (int j = 0; j < a->ncols; j++) {
            MATD_EL(a, i, j) *= s;
        }
    }
}

matd_t *matd_add(const matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(a->nrows == b->nrows);
    assert(a->ncols == b->ncols);

    if (matd_is_scalar(a))
        return matd_create_scalar(a->data[0] + b->data[0]);

    matd_t *m = matd_create(a->nrows, a->ncols);

    for (int i = 0; i < m->nrows; i++) {
        for (int j = 0; j < m->ncols; j++) {
            MATD_EL(m, i, j) = MATD_EL(a, i, j) + MATD_EL(b, i, j);
        }
    }

    return m;
}

void matd_add_inplace(matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(a->nrows == b->nrows);
    assert(a->ncols == b->ncols);

    if (matd_is_scalar(a)) {
        a->data[0] += b->data[0];
        return;
    }

    for (int i = 0; i < a->nrows; i++) {
        for (int j = 0; j < a->ncols; j++) {
            MATD_EL(a, i, j) += MATD_EL(b, i, j);
        }
    }
}


matd_t *matd_subtract(const matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(a->nrows == b->nrows);
    assert(a->ncols == b->ncols);

    if (matd_is_scalar(a))
        return matd_create_scalar(a->data[0] - b->data[0]);

    matd_t *m = matd_create(a->nrows, a->ncols);

    for (int i = 0; i < m->nrows; i++) {
        for (int j = 0; j < m->ncols; j++) {
            MATD_EL(m, i, j) = MATD_EL(a, i, j) - MATD_EL(b, i, j);
        }
    }

    return m;
}

void matd_subtract_inplace(matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(a->nrows == b->nrows);
    assert(a->ncols == b->ncols);

    if (matd_is_scalar(a)) {
        a->data[0] -= b->data[0];
        return;
    }

    for (int i = 0; i < a->nrows; i++) {
        for (int j = 0; j < a->ncols; j++) {
            MATD_EL(a, i, j) -= MATD_EL(b, i, j);
        }
    }
}


matd_t *matd_transpose(const matd_t *a)
{
    assert(a != NULL);

    if (matd_is_scalar(a))
        return matd_create_scalar(a->data[0]);

    matd_t *m = matd_create(a->ncols, a->nrows);

    for (int i = 0; i < a->nrows; i++) {
        for (int j = 0; j < a->ncols; j++) {
            MATD_EL(m, j, i) = MATD_EL(a, i, j);
        }
    }
    return m;
}

static
double matd_det_general(const matd_t *a)
{
    // Use LU decompositon to calculate the determinant
    matd_plu_t *mlu = matd_plu(a);
    matd_t *L = matd_plu_l(mlu);
    matd_t *U = matd_plu_u(mlu);

    // The determinants of the L and U matrices are the products of
    // their respective diagonal elements
    double detL = 1; double detU = 1;
    for (int i = 0; i < a->nrows; i++) {
        detL *= matd_get(L, i, i);
        detU *= matd_get(U, i, i);
    }

    // The determinant of a can be calculated as
    //     epsilon*det(L)*det(U),
    // where epsilon is just the sign of the corresponding permutation
    // (which is +1 for an even number of permutations and is −1
    // for an uneven number of permutations).
    double det = mlu->pivsign * detL * detU;

    // Cleanup
    matd_plu_destroy(mlu);
    matd_destroy(L);
    matd_destroy(U);

    return det;
}

double matd_det(const matd_t *a)
{
    assert(a != NULL);
    assert(a->nrows == a->ncols);

    switch(a->nrows) {
        case 0:
            // scalar: invalid
            assert(a->nrows > 0);
            break;

        case 1:
            // 1x1 matrix
            return a->data[0];

        case 2:
            // 2x2 matrix
            return a->data[0] * a->data[3] - a->data[1] * a->data[2];

        case 3:
            // 3x3 matrix
            return  a->data[0]*a->data[4]*a->data[8]
                - a->data[0]*a->data[5]*a->data[7]
                + a->data[1]*a->data[5]*a->data[6]
                - a->data[1]*a->data[3]*a->data[8]
                + a->data[2]*a->data[3]*a->data[7]
                - a->data[2]*a->data[4]*a->data[6];

        case 4: {
            // 4x4 matrix
            double m00 = MATD_EL(a,0,0), m01 = MATD_EL(a,0,1), m02 = MATD_EL(a,0,2), m03 = MATD_EL(a,0,3);
            double m10 = MATD_EL(a,1,0), m11 = MATD_EL(a,1,1), m12 = MATD_EL(a,1,2), m13 = MATD_EL(a,1,3);
            double m20 = MATD_EL(a,2,0), m21 = MATD_EL(a,2,1), m22 = MATD_EL(a,2,2), m23 = MATD_EL(a,2,3);
            double m30 = MATD_EL(a,3,0), m31 = MATD_EL(a,3,1), m32 = MATD_EL(a,3,2), m33 = MATD_EL(a,3,3);

            return m00 * m11 * m22 * m33 - m00 * m11 * m23 * m32 -
                m00 * m21 * m12 * m33 + m00 * m21 * m13 * m32 + m00 * m31 * m12 * m23 -
                m00 * m31 * m13 * m22 - m10 * m01 * m22 * m33 +
                m10 * m01 * m23 * m32 + m10 * m21 * m02 * m33 -
                m10 * m21 * m03 * m32 - m10 * m31 * m02 * m23 +
                m10 * m31 * m03 * m22 + m20 * m01 * m12 * m33 -
                m20 * m01 * m13 * m32 - m20 * m11 * m02 * m33 +
                m20 * m11 * m03 * m32 + m20 * m31 * m02 * m13 -
                m20 * m31 * m03 * m12 - m30 * m01 * m12 * m23 +
                m30 * m01 * m13 * m22 + m30 * m11 * m02 * m23 -
                m30 * m11 * m03 * m22 - m30 * m21 * m02 * m13 +
                m30 * m21 * m03 * m12;
        }

        default:
            return matd_det_general(a);
    }

    assert(0);
    return 0;
}

// returns NULL if the matrix is (exactly) singular. Caller is
// otherwise responsible for knowing how to cope with badly
// conditioned matrices.
matd_t *matd_inverse(const matd_t *x)
{
    matd_t *m = NULL;

    assert(x != NULL);
    assert(x->nrows == x->ncols);

    if (matd_is_scalar(x)) {
        if (x->data[0] == 0)
            return NULL;

        return matd_create_scalar(1.0 / x->data[0]);
    }

    switch(x->nrows) {
        case 1: {
            double det = x->data[0];
            if (det == 0)
                return NULL;

            double invdet = 1.0 / det;

            m = matd_create(x->nrows, x->nrows);
            MATD_EL(m, 0, 0) = 1.0 * invdet;
            return m;
        }

        case 2: {
            double det = x->data[0] * x->data[3] - x->data[1] * x->data[2];
            if (det == 0)
                return NULL;

            double invdet = 1.0 / det;

            m = matd_create(x->nrows, x->nrows);
            MATD_EL(m, 0, 0) = MATD_EL(x, 1, 1) * invdet;
            MATD_EL(m, 0, 1) = - MATD_EL(x, 0, 1) * invdet;
            MATD_EL(m, 1, 0) = - MATD_EL(x, 1, 0) * invdet;
            MATD_EL(m, 1, 1) = MATD_EL(x, 0, 0) * invdet;
            return m;
        }

        default: {
            matd_plu_t *plu = matd_plu(x);

            matd_t *inv = NULL;
            if (!plu->singular) {
                matd_t *ident = matd_identity(x->nrows);
                inv = matd_plu_solve(plu, ident);
                matd_destroy(ident);
            }

            matd_plu_destroy(plu);

            return inv;
        }
    }

    return NULL; // unreachable
}



// TODO Optimization: Some operations we could perform in-place,
// saving some memory allocation work. E.g., ADD, SUBTRACT. Just need
// to make sure that we don't do an in-place modification on a matrix
// that was an input argument!

// handle right-associative operators, greedily consuming them. These
// include transpose and inverse. This is called by the main recursion
// method.
static inline matd_t *matd_op_gobble_right(const char *expr, int *pos, matd_t *acc, matd_t **garb, int *garbpos)
{
    while (expr[*pos] != 0) {

        switch (expr[*pos]) {

            case '\'': {
                assert(acc != NULL); // either a syntax error or a math op failed, producing null
                matd_t *res = matd_transpose(acc);
                garb[*garbpos] = res;
                (*garbpos)++;
                acc = res;

                (*pos)++;
                break;
            }

                // handle inverse ^-1. No other exponents are allowed.
            case '^': {
                assert(acc != NULL);
                assert(expr[*pos+1] == '-');
                assert(expr[*pos+2] == '1');

                matd_t *res = matd_inverse(acc);
                garb[*garbpos] = res;
                (*garbpos)++;
                acc = res;

                (*pos)+=3;
                break;
            }

            default:
                return acc;
        }
    }

    return acc;
}

// @garb, garbpos  A list of every matrix allocated during evaluation... used to assist cleanup.
// @oneterm: we should return at the end of this term (i.e., stop at a PLUS, MINUS, LPAREN).
static matd_t *matd_op_recurse(const char *expr, int *pos, matd_t *acc, matd_t **args, int *argpos,
                               matd_t **garb, int *garbpos, int oneterm)
{
    while (expr[*pos] != 0) {

        switch (expr[*pos]) {

            case '(': {
                if (oneterm && acc != NULL)
                    return acc;
                (*pos)++;
                matd_t *rhs = matd_op_recurse(expr, pos, NULL, args, argpos, garb, garbpos, 0);
                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                if (acc == NULL) {
                    acc = rhs;
                } else {
                    matd_t *res = matd_multiply(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }

                break;
            }

            case ')': {
                if (oneterm)
                    return acc;

                (*pos)++;
                return acc;
            }

            case '*': {
                (*pos)++;

                matd_t *rhs = matd_op_recurse(expr, pos, NULL, args, argpos, garb, garbpos, 1);
                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                if (acc == NULL) {
                    acc = rhs;
                } else {
                    matd_t *res = matd_multiply(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }

                break;
            }

            case 'F': {
                matd_t *rhs = args[*argpos];
                garb[*garbpos] = rhs;
                (*garbpos)++;

                (*pos)++;
                (*argpos)++;

                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                if (acc == NULL) {
                    acc = rhs;
                } else {
                    matd_t *res = matd_multiply(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }

                break;
            }

            case 'M': {
                matd_t *rhs = args[*argpos];

                (*pos)++;
                (*argpos)++;

                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                if (acc == NULL) {
                    acc = rhs;
                } else {
                    matd_t *res = matd_multiply(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }

                break;
            }

/*
  case 'D': {
  int rows = expr[*pos+1]-'0';
  int cols = expr[*pos+2]-'0';

  matd_t *rhs = matd_create(rows, cols);

  break;
  }
*/
                // a constant (SCALAR) defined inline. Treat just like M, creating a matd_t on the fly.
            case '0':
            case '1':
            case '2':
            case '3':
            case '4':
            case '5':
            case '6':
            case '7':
            case '8':
            case '9':
            case '.': {
                const char *start = &expr[*pos];
                char *end;
                double s = strtod(start, &end);
                (*pos) += (end - start);
                matd_t *rhs = matd_create_scalar(s);
                garb[*garbpos] = rhs;
                (*garbpos)++;

                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                if (acc == NULL) {
                    acc = rhs;
                } else {
                    matd_t *res = matd_multiply(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }

                break;
            }

            case '+': {
                if (oneterm && acc != NULL)
                    return acc;

                // don't support unary plus
                assert(acc != NULL);
                (*pos)++;
                matd_t *rhs = matd_op_recurse(expr, pos, NULL, args, argpos, garb, garbpos, 1);
                rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                matd_t *res = matd_add(acc, rhs);

                garb[*garbpos] = res;
                (*garbpos)++;
                acc = res;
                break;
            }

            case '-': {
                if (oneterm && acc != NULL)
                    return acc;

                if (acc == NULL) {
                    // unary minus
                    (*pos)++;
                    matd_t *rhs = matd_op_recurse(expr, pos, NULL, args, argpos, garb, garbpos, 1);
                    rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                    matd_t *res = matd_scale(rhs, -1);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                } else {
                    // subtract
                    (*pos)++;
                    matd_t *rhs = matd_op_recurse(expr, pos, NULL, args, argpos, garb, garbpos, 1);
                    rhs = matd_op_gobble_right(expr, pos, rhs, garb, garbpos);

                    matd_t *res = matd_subtract(acc, rhs);
                    garb[*garbpos] = res;
                    (*garbpos)++;
                    acc = res;
                }
                break;
            }

            case ' ': {
                // nothing to do. spaces are meaningless.
                (*pos)++;
                break;
            }

            default: {
                debug_print("Unknown character: '%c'\n", expr[*pos]);
                assert(expr[*pos] != expr[*pos]);
            }
        }
    }
    return acc;
}

// always returns a new matrix.
matd_t *matd_op(const char *expr, ...)
{
    int nargs = 0;
    int exprlen = 0;

    assert(expr != NULL);

    for (const char *p = expr; *p != 0; p++) {
        if (*p == 'M' || *p == 'F')
            nargs++;
        exprlen++;
    }

    assert(nargs > 0);

    if (!exprlen) // expr = ""
        return NULL;

    va_list ap;
    va_start(ap, expr);

    matd_t **args = malloc(sizeof(matd_t*)*nargs);
    for (int i = 0; i < nargs; i++) {
        args[i] = va_arg(ap, matd_t*);
        // XXX: sanity check argument; emit warning/error if args[i]
        // doesn't look like a matd_t*.
    }

    va_end(ap);

    int pos = 0;
    int argpos = 0;
    int garbpos = 0;

    // can't create more than 2 new result per character
    // one result, and possibly one argument to free
    matd_t **garb = malloc(sizeof(matd_t*)*2*exprlen);

    matd_t *res = matd_op_recurse(expr, &pos, NULL, args, &argpos, garb, &garbpos, 0);
    free(args);

    // 'res' may need to be freed as part of garbage collection (i.e. expr = "F")
    matd_t *res_copy = (res ? matd_copy(res) : NULL);

    for (int i = 0; i < garbpos; i++) {
        matd_destroy(garb[i]);
    }
    free(garb);

    return res_copy;
}

double matd_vec_mag(const matd_t *a)
{
    assert(a != NULL);
    assert(matd_is_vector(a));

    double mag = 0.0;
    int len = a->nrows*a->ncols;
    for (int i = 0; i < len; i++)
        mag += sq(a->data[i]);
    return sqrt(mag);
}

double matd_vec_dist(const matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(matd_is_vector(a) && matd_is_vector(b));
    assert(a->nrows*a->ncols == b->nrows*b->ncols);

    int lena = a->nrows*a->ncols;
    return matd_vec_dist_n(a, b, lena);
}

double matd_vec_dist_n(const matd_t *a, const matd_t *b, int n)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(matd_is_vector(a) && matd_is_vector(b));

    int lena = a->nrows*a->ncols;
    int lenb = b->nrows*b->ncols;

    assert(n <= lena && n <= lenb);

    double mag = 0.0;
    for (int i = 0; i < n; i++)
        mag += sq(a->data[i] - b->data[i]);
    return sqrt(mag);
}

// find the index of the off-diagonal element with the largest mag
static inline int max_idx(const matd_t *A, int row, int maxcol)
{
    int maxi = 0;
    double maxv = -1;

    for (int i = 0; i < maxcol; i++) {
        if (i == row)
            continue;
        double v = fabs(MATD_EL(A, row, i));
        if (v > maxv) {
            maxi = i;
            maxv = v;
        }
    }

    return maxi;
}

double matd_vec_dot_product(const matd_t *a, const matd_t *b)
{
    assert(a != NULL);
    assert(b != NULL);
    assert(matd_is_vector(a) && matd_is_vector(b));
    int adim = a->ncols*a->nrows;
    int bdim = b->ncols*b->nrows;
    assert(adim == bdim);

    double acc = 0;
    for (int i = 0; i < adim; i++) {
        acc += a->data[i] * b->data[i];
    }
    return acc;
}


matd_t *matd_vec_normalize(const matd_t *a)
{
    assert(a != NULL);
    assert(matd_is_vector(a));

    double mag = matd_vec_mag(a);
    assert(mag > 0);

    matd_t *b = matd_create(a->nrows, a->ncols);

    int len = a->nrows*a->ncols;
    for(int i = 0; i < len; i++)
        b->data[i] = a->data[i] / mag;

    return b;
}

matd_t *matd_crossproduct(const matd_t *a, const matd_t *b)
{ // only defined for vecs (col or row) of length 3
    assert(a != NULL);
    assert(b != NULL);
    assert(matd_is_vector_len(a, 3) && matd_is_vector_len(b, 3));

    matd_t * r = matd_create(a->nrows, a->ncols);

    r->data[0] = a->data[1] * b->data[2] - a->data[2] * b->data[1];
    r->data[1] = a->data[2] * b->data[0] - a->data[0] * b->data[2];
    r->data[2] = a->data[0] * b->data[1] - a->data[1] * b->data[0];

    return r;
}

TYPE matd_err_inf(const matd_t *a, const matd_t *b)
{
    assert(a->nrows == b->nrows);
    assert(a->ncols == b->ncols);

    TYPE maxf = 0;

    for (int i = 0; i < a->nrows; i++) {
        for (int j = 0; j < a->ncols; j++) {
            TYPE av = MATD_EL(a, i, j);
            TYPE bv = MATD_EL(b, i, j);

            TYPE err = fabs(av - bv);
            maxf = fmax(maxf, err);
        }
    }

    return maxf;
}

// Computes an SVD for square or tall matrices. This code doesn't work
// for wide matrices, because the bidiagonalization results in one
// non-zero element too far to the right for us to rotate away.
//
// Caller is responsible for destroying U, S, and V.
static matd_svd_t matd_svd_tall(matd_t *A, int flags)
{
    matd_t *B = matd_copy(A);

    // Apply householder reflections on each side to reduce A to
    // bidiagonal form. Specifically:
    //
    // A = LS*B*RS'
    //
    // Where B is bidiagonal, and LS/RS are unitary.
    //
    // Why are we doing this? Some sort of transformation is necessary
    // to reduce the matrix's nz elements to a square region. QR could
    // work too. We need nzs confined to a square region so that the
    // subsequent iterative process, which is based on rotations, can
    // work. (To zero out a term at (i,j), our rotations will also
    // affect (j,i).
    //
    // We prefer bidiagonalization over QR because it gets us "closer"
    // to the SVD, which should mean fewer iterations.

    // LS: cumulative left-handed transformations
    matd_t *LS = matd_identity(A->nrows);

    // RS: cumulative right-handed transformations.
    matd_t *RS = matd_identity(A->ncols);

    for (int hhidx = 0; hhidx < A->nrows; hhidx++)  {

        if (hhidx < A->ncols) {
            // We construct the normal of the reflection plane: let u
            // be the vector to reflect, x =[ M 0 0 0 ] the target
            // location for u (u') after reflection (with M = ||u||).
            //
            // The normal vector is then n = (u - x), but since we
            // could equally have the target location be x = [-M 0 0 0
            // ], we could use n = (u + x).
            //
            // We then normalize n. To ensure a reasonable magnitude,
            // we select the sign of M so as to maximize the magnitude
            // of the first element of (x +/- M). (Otherwise, we could
            // end up with a divide-by-zero if u[0] and M cancel.)
            //
            // The householder reflection matrix is then H=(I - nn'), and
            // u' = Hu.
            //
            //
            int vlen = A->nrows - hhidx;

            double *v = malloc(sizeof(double)*vlen);

            double mag2 = 0;
            for (int i = 0; i < vlen; i++) {
                v[i] = MATD_EL(B, hhidx+i, hhidx);
                mag2 += v[i]*v[i];
            }

            double oldv0 = v[0];
            if (oldv0 < 0)
                v[0] -= sqrt(mag2);
            else
                v[0] += sqrt(mag2);

            mag2 += -oldv0*oldv0 + v[0]*v[0];

            // normalize v
            double mag = sqrt(mag2);

            // this case arises with matrices of all zeros, for example.
            if (mag == 0) {
                free(v);
                continue;
            }

            for (int i = 0; i < vlen; i++)
                v[i] /= mag;

            // Q = I - 2vv'
            //matd_t *Q = matd_identity(A->nrows);
            //for (int i = 0; i < vlen; i++)
            //  for (int j = 0; j < vlen; j++)
            //    MATD_EL(Q, i+hhidx, j+hhidx) -= 2*v[i]*v[j];


            // LS = matd_op("F*M", LS, Q);
            // Implementation: take each row of LS, compute dot product with n,
            // subtract n (scaled by dot product) from it.
            for (int i = 0; i < LS->nrows; i++) {
                double dot = 0;
                for (int j = 0; j < vlen; j++)
                    dot += MATD_EL(LS, i, hhidx+j) * v[j];
                for (int j = 0; j < vlen; j++)
                    MATD_EL(LS, i, hhidx+j) -= 2*dot*v[j];
            }

            //  B = matd_op("M*F", Q, B); // should be Q', but Q is symmetric.
            for (int i = 0; i < B->ncols; i++) {
                double dot = 0;
                for (int j = 0; j < vlen; j++)
                    dot += MATD_EL(B, hhidx+j, i) * v[j];
                for (int j = 0; j < vlen; j++)
                    MATD_EL(B, hhidx+j, i) -= 2*dot*v[j];
            }

            free(v);
        }

        if (hhidx+2 < A->ncols) {
            int vlen = A->ncols - hhidx - 1;

            double *v = malloc(sizeof(double)*vlen);

            double mag2 = 0;
            for (int i = 0; i < vlen; i++) {
                v[i] = MATD_EL(B, hhidx, hhidx+i+1);
                mag2 += v[i]*v[i];
            }

            double oldv0 = v[0];
            if (oldv0 < 0)
                v[0] -= sqrt(mag2);
            else
                v[0] += sqrt(mag2);

            mag2 += -oldv0*oldv0 + v[0]*v[0];

            // compute magnitude of ([1 0 0..]+v)
            double mag = sqrt(mag2);

            // this case can occur when the vectors are already perpendicular
            if (mag == 0) {
                free(v);
                continue;
            }

            for (int i = 0; i < vlen; i++)
                v[i] /= mag;

            // TODO: optimize these multiplications
            // matd_t *Q = matd_identity(A->ncols);
            //  for (int i = 0; i < vlen; i++)
            //    for (int j = 0; j < vlen; j++)
            //       MATD_EL(Q, i+1+hhidx, j+1+hhidx) -= 2*v[i]*v[j];

            //  RS = matd_op("F*M", RS, Q);
            for (int i = 0; i < RS->nrows; i++) {
                double dot = 0;
                for (int j = 0; j < vlen; j++)
                    dot += MATD_EL(RS, i, hhidx+1+j) * v[j];
                for (int j = 0; j < vlen; j++)
                    MATD_EL(RS, i, hhidx+1+j) -= 2*dot*v[j];
            }

            //   B = matd_op("F*M", B, Q); // should be Q', but Q is symmetric.
            for (int i = 0; i < B->nrows; i++) {
                double dot = 0;
                for (int j = 0; j < vlen; j++)
                    dot += MATD_EL(B, i, hhidx+1+j) * v[j];
                for (int j = 0; j < vlen; j++)
                    MATD_EL(B, i, hhidx+1+j) -= 2*dot*v[j];
            }

            free(v);
        }
    }

    // empirically, we find a roughly linear worst-case number of iterations
    // as a function of rows*cols. maxiters ~= 1.5*nrows*ncols
    // we're a bit conservative below.
    int maxiters = 200 + 2 * A->nrows * A->ncols;
    assert(maxiters > 0); // reassure clang
    int iter;

    double maxv; // maximum non-zero value being reduced this iteration

    double tol = 1E-10;

    // which method will we use to find the largest off-diagonal
    // element of B?
    const int find_max_method = 1; //(B->ncols < 6) ? 2 : 1;

    // for each of the first B->ncols rows, which index has the
    // maximum absolute value? (used by method 1)
    int *maxrowidx = malloc(sizeof(int)*B->ncols);
    int lastmaxi, lastmaxj;

    if (find_max_method == 1) {
        for (int i = 2; i < B->ncols; i++)
            maxrowidx[i] = max_idx(B, i, B->ncols);

        // note that we started the array at 2. That's because by setting
        // these values below, we'll recompute first two entries on the
        // first iteration!
        lastmaxi = 0, lastmaxj = 1;
    }

    for (iter = 0; iter < maxiters; iter++) {

        // No diagonalization required for 0x0 and 1x1 matrices.
        if (B->ncols < 2)
            break;

        // find the largest off-diagonal element of B, and put its
        // coordinates in maxi, maxj.
        int maxi, maxj;

        if (find_max_method == 1) {
            // method 1 is the "smarter" method which does at least
            // 4*ncols work. More work might be needed (up to
            // ncols*ncols), depending on data. Thus, this might be a
            // bit slower than the default method for very small
            // matrices.
            maxi = -1;
            maxv = -1;

            // every iteration, we must deal with the fact that rows
            // and columns lastmaxi and lastmaxj have been
            // modified. Update maxrowidx accordingly.

            // now, EVERY row also had columns lastmaxi and lastmaxj modified.
            for (int rowi = 0; rowi < B->ncols; rowi++) {

                // the magnitude of the largest off-diagonal element
                // in this row.
                double thismaxv;

                // row 'lastmaxi' and 'lastmaxj' have been completely
                // changed. compute from scratch.
                if (rowi == lastmaxi || rowi == lastmaxj) {
                    maxrowidx[rowi] = max_idx(B, rowi, B->ncols);
                    thismaxv = fabs(MATD_EL(B, rowi, maxrowidx[rowi]));
                    goto endrowi;
                }

                // our maximum entry was just modified. We don't know
                // if it went up or down, and so we don't know if it
                // is still the maximum. We have to update from
                // scratch.
                if (maxrowidx[rowi] == lastmaxi || maxrowidx[rowi] == lastmaxj) {
                    maxrowidx[rowi] = max_idx(B, rowi, B->ncols);
                    thismaxv = fabs(MATD_EL(B, rowi, maxrowidx[rowi]));
                    goto endrowi;
                }

                // This row is unchanged, except for columns
                // 'lastmaxi' and 'lastmaxj', and those columns were
                // not previously the largest entry...  just check to
                // see if they are now the maximum entry in their
                // row. (Remembering to consider off-diagonal entries
                // only!)
                thismaxv = fabs(MATD_EL(B, rowi, maxrowidx[rowi]));

                // check column lastmaxi. Is it now the maximum?
                if (lastmaxi != rowi) {
                    double v = fabs(MATD_EL(B, rowi, lastmaxi));
                    if (v > thismaxv) {
                        thismaxv = v;
                        maxrowidx[rowi] = lastmaxi;
                    }
                }

                // check column lastmaxj
                if (lastmaxj != rowi) {
                    double v = fabs(MATD_EL(B, rowi, lastmaxj));
                    if (v > thismaxv) {
                        thismaxv = v;
                        maxrowidx[rowi] = lastmaxj;
                    }
                }

                // does this row have the largest value we've seen so far?
              endrowi:
                if (thismaxv > maxv) {
                    maxv = thismaxv;
                    maxi = rowi;
                }
            }

            assert(maxi >= 0);
            maxj = maxrowidx[maxi];

            // save these for the next iteration.
            lastmaxi = maxi;
            lastmaxj = maxj;

            if (maxv < tol)
                break;

        } else if (find_max_method == 2) {
            // brute-force (reference) version.
            maxv = -1;

            // only search top "square" portion
            for (int i = 0; i < B->ncols; i++) {
                for (int j = 0; j < B->ncols; j++) {
                    if (i == j)
                        continue;

                    double v = fabs(MATD_EL(B, i, j));

                    if (v > maxv) {
                        maxi = i;
                        maxj = j;
                        maxv = v;
                    }
                }
            }

            // termination condition.
            if (maxv < tol)
                break;
        } else {
            assert(0);
        }

//        printf(">>> %5d %3d, %3d %15g\n", maxi, maxj, iter, maxv);

        // Now, solve the 2x2 SVD problem for the matrix
        // [ A0 A1 ]
        // [ A2 A3 ]
        double A0 = MATD_EL(B, maxi, maxi);
        double A1 = MATD_EL(B, maxi, maxj);
        double A2 = MATD_EL(B, maxj, maxi);
        double A3 = MATD_EL(B, maxj, maxj);

        if (1) {
            double AQ[4];
            AQ[0] = A0;
            AQ[1] = A1;
            AQ[2] = A2;
            AQ[3] = A3;

            double U[4], S[2], V[4];
            svd22(AQ, U, S, V);

/*  Reference (slow) implementation...

            // LS = LS * ROT(theta) = LS * QL
            matd_t *QL = matd_identity(A->nrows);
            MATD_EL(QL, maxi, maxi) = U[0];
            MATD_EL(QL, maxi, maxj) = U[1];
            MATD_EL(QL, maxj, maxi) = U[2];
            MATD_EL(QL, maxj, maxj) = U[3];

            matd_t *QR = matd_identity(A->ncols);
            MATD_EL(QR, maxi, maxi) = V[0];
            MATD_EL(QR, maxi, maxj) = V[1];
            MATD_EL(QR, maxj, maxi) = V[2];
            MATD_EL(QR, maxj, maxj) = V[3];

            LS = matd_op("F*M", LS, QL);
            RS = matd_op("F*M", RS, QR); // remember we'll transpose RS.
            B = matd_op("M'*F*M", QL, B, QR);

            matd_destroy(QL);
            matd_destroy(QR);
*/

            //  LS = matd_op("F*M", LS, QL);
            for (int i = 0; i < LS->nrows; i++) {
                double vi = MATD_EL(LS, i, maxi);
                double vj = MATD_EL(LS, i, maxj);

                MATD_EL(LS, i, maxi) = U[0]*vi + U[2]*vj;
                MATD_EL(LS, i, maxj) = U[1]*vi + U[3]*vj;
            }

            //  RS = matd_op("F*M", RS, QR); // remember we'll transpose RS.
            for (int i = 0; i < RS->nrows; i++) {
                double vi = MATD_EL(RS, i, maxi);
                double vj = MATD_EL(RS, i, maxj);

                MATD_EL(RS, i, maxi) = V[0]*vi + V[2]*vj;
                MATD_EL(RS, i, maxj) = V[1]*vi + V[3]*vj;
            }

            // B = matd_op("M'*F*M", QL, B, QR);
            // The QL matrix mixes rows of B.
            for (int i = 0; i < B->ncols; i++) {
                double vi = MATD_EL(B, maxi, i);
                double vj = MATD_EL(B, maxj, i);

                MATD_EL(B, maxi, i) = U[0]*vi + U[2]*vj;
                MATD_EL(B, maxj, i) = U[1]*vi + U[3]*vj;
            }

            // The QR matrix mixes columns of B.
            for (int i = 0; i < B->nrows; i++) {
                double vi = MATD_EL(B, i, maxi);
                double vj = MATD_EL(B, i, maxj);

                MATD_EL(B, i, maxi) = V[0]*vi + V[2]*vj;
                MATD_EL(B, i, maxj) = V[1]*vi + V[3]*vj;
            }
        }
    }

    free(maxrowidx);

    if (!(flags & MATD_SVD_NO_WARNINGS) && iter == maxiters) {
        debug_print("WARNING: maximum iters (maximum = %d, matrix %d x %d, max=%.15f)\n",
               iter, A->nrows, A->ncols, maxv);

//        matd_print(A, "%15f");
    }

    // them all positive by flipping the corresponding columns of
    // U/LS.
    int *idxs = malloc(sizeof(int)*A->ncols);
    double *vals = malloc(sizeof(double)*A->ncols);
    for (int i = 0; i < A->ncols; i++) {
        idxs[i] = i;
        vals[i] = MATD_EL(B, i, i);
    }

    // A bubble sort. Seriously.
    int changed;
    do {
        changed = 0;

        for (int i = 0; i + 1 < A->ncols; i++) {
            if (fabs(vals[i+1]) > fabs(vals[i])) {
                int tmpi = idxs[i];
                idxs[i] = idxs[i+1];
                idxs[i+1] = tmpi;

                double tmpv = vals[i];
                vals[i] = vals[i+1];
                vals[i+1] = tmpv;

                changed = 1;
            }
        }
    } while (changed);

    matd_t *LP = matd_identity(A->nrows);
    matd_t *RP = matd_identity(A->ncols);

    for (int i = 0; i < A->ncols; i++) {
        MATD_EL(LP, idxs[i], idxs[i]) = 0; // undo the identity above
        MATD_EL(RP, idxs[i], idxs[i]) = 0;

        MATD_EL(LP, idxs[i], i) = vals[i] < 0 ? -1 : 1;
        MATD_EL(RP, idxs[i], i) = 1; //vals[i] < 0 ? -1 : 1;
    }
    free(idxs);
    free(vals);

    // we've factored:
    // LP*(something)*RP'

    // solve for (something)
    B = matd_op("M'*F*M", LP, B, RP);

    // update LS and RS, remembering that RS will be transposed.
    LS = matd_op("F*M", LS, LP);
    RS = matd_op("F*M", RS, RP);

    matd_destroy(LP);
    matd_destroy(RP);

    matd_svd_t res;
    memset(&res, 0, sizeof(res));

    // make B exactly diagonal

    for (int i = 0; i < B->nrows; i++) {
        for (int j = 0; j < B->ncols; j++) {
            if (i != j)
                MATD_EL(B, i, j) = 0;
        }
    }

    res.U = LS;
    res.S = B;
    res.V = RS;

    return res;
}

matd_svd_t matd_svd(matd_t *A)
{
    return matd_svd_flags(A, 0);
}

matd_svd_t matd_svd_flags(matd_t *A, int flags)
{
    matd_svd_t res;

    if (A->ncols <= A->nrows) {
        res = matd_svd_tall(A, flags);
    } else {
        matd_t *At = matd_transpose(A);

        // A =U  S  V'
        // A'=V  S' U'

        matd_svd_t tmp = matd_svd_tall(At, flags);

        memset(&res, 0, sizeof(res));
        res.U = tmp.V; //matd_transpose(tmp.V);
        res.S = matd_transpose(tmp.S);
        res.V = tmp.U; //matd_transpose(tmp.U);

        matd_destroy(tmp.S);
        matd_destroy(At);
    }

/*
  matd_t *check = matd_op("M*M*M'-M", res.U, res.S, res.V, A);
  double maxerr = 0;

  for (int i = 0; i < check->nrows; i++)
  for (int j = 0; j < check->ncols; j++)
  maxerr = fmax(maxerr, fabs(MATD_EL(check, i, j)));

  matd_destroy(check);

  if (maxerr > 1e-7) {
  printf("bad maxerr: %15f\n", maxerr);
  }

  if (maxerr > 1e-5) {
  printf("bad maxerr: %15f\n", maxerr);
  matd_print(A, "%15f");
  assert(0);
  }

*/
    return res;
}


matd_plu_t *matd_plu(const matd_t *a)
{
    unsigned int *piv = calloc(a->nrows, sizeof(unsigned int));
    int pivsign = 1;
    matd_t *lu = matd_copy(a);

    // only for square matrices.
    assert(a->nrows == a->ncols);

    matd_plu_t *mlu = calloc(1, sizeof(matd_plu_t));

    for (int i = 0; i < a->nrows; i++)
        piv[i] = i;

    for (int j = 0; j < a->ncols; j++) {
        for (int i = 0; i < a->nrows; i++) {
            int kmax = i < j ? i : j; // min(i,j)

            // compute dot product of row i with column j (up through element kmax)
            double acc = 0;
            for (int k = 0; k < kmax; k++)
                acc += MATD_EL(lu, i, k) * MATD_EL(lu, k, j);

            MATD_EL(lu, i, j) -= acc;
        }

        // find pivot and exchange if necessary.
        int p = j;
        if (1) {
            for (int i = j+1; i < lu->nrows; i++) {
                if (fabs(MATD_EL(lu,i,j)) > fabs(MATD_EL(lu, p, j))) {
                    p = i;
                }
            }
        }

        // swap rows p and j?
        if (p != j) {
            TYPE *tmp = malloc(sizeof(TYPE)*lu->ncols);
            memcpy(tmp, &MATD_EL(lu, p, 0), sizeof(TYPE) * lu->ncols);
            memcpy(&MATD_EL(lu, p, 0), &MATD_EL(lu, j, 0), sizeof(TYPE) * lu->ncols);
            memcpy(&MATD_EL(lu, j, 0), tmp, sizeof(TYPE) * lu->ncols);
            int k = piv[p];
            piv[p] = piv[j];
            piv[j] = k;
            pivsign = -pivsign;
            free(tmp);
        }

        double LUjj = MATD_EL(lu, j, j);

        // If our pivot is very small (which means the matrix is
        // singular or nearly singular), replace with a new pivot of the
        // right sign.
        if (fabs(LUjj) < MATD_EPS) {
/*
            if (LUjj < 0)
                LUjj = -MATD_EPS;
            else
                LUjj = MATD_EPS;

            MATD_EL(lu, j, j) = LUjj;
*/
            mlu->singular = 1;
        }

        if (j < lu->ncols && j < lu->nrows && LUjj != 0) {
            LUjj = 1.0 / LUjj;
            for (int i = j+1; i < lu->nrows; i++)
                MATD_EL(lu, i, j) *= LUjj;
        }
    }

    mlu->lu = lu;
    mlu->piv = piv;
    mlu->pivsign = pivsign;

    return mlu;
}

void matd_plu_destroy(matd_plu_t *mlu)
{
    matd_destroy(mlu->lu);
    free(mlu->piv);
    memset(mlu, 0, sizeof(matd_plu_t));
    free(mlu);
}

double matd_plu_det(const matd_plu_t *mlu)
{
    matd_t *lu = mlu->lu;
    double det = mlu->pivsign;

    if (lu->nrows == lu->ncols) {
        for (int i = 0; i < lu->ncols; i++)
            det *= MATD_EL(lu, i, i);
    }

    return det;
}

matd_t *matd_plu_p(const matd_plu_t *mlu)
{
    matd_t *lu = mlu->lu;
    matd_t *P = matd_create(lu->nrows, lu->nrows);

    for (int i = 0; i < lu->nrows; i++) {
        MATD_EL(P, mlu->piv[i], i) = 1;
    }

    return P;
}

matd_t *matd_plu_l(const matd_plu_t *mlu)
{
    matd_t *lu = mlu->lu;

    matd_t *L = matd_create(lu->nrows, lu->ncols);
    for (int i = 0; i < lu->nrows; i++) {
        MATD_EL(L, i, i) = 1;

        for (int j = 0; j < i; j++) {
            MATD_EL(L, i, j) = MATD_EL(lu, i, j);
        }
    }

    return L;
}

matd_t *matd_plu_u(const matd_plu_t *mlu)
{
    matd_t *lu = mlu->lu;

    matd_t *U = matd_create(lu->ncols, lu->ncols);
    for (int i = 0; i < lu->ncols; i++) {
        for (int j = 0; j < lu->ncols; j++) {
            if (i <= j)
                MATD_EL(U, i, j) = MATD_EL(lu, i, j);
        }
    }

    return U;
}

// PLU = A
// Ax = B
// PLUx = B
// LUx = P'B
matd_t *matd_plu_solve(const matd_plu_t *mlu, const matd_t *b)
{
    matd_t *x = matd_copy(b);

    // permute right hand side
    for (int i = 0; i < mlu->lu->nrows; i++)
        memcpy(&MATD_EL(x, i, 0), &MATD_EL(b, mlu->piv[i], 0), sizeof(TYPE) * b->ncols);

    // solve Ly = b
    for (int k = 0; k < mlu->lu->nrows; k++) {
        for (int i = k+1; i < mlu->lu->nrows; i++) {
            double LUik = -MATD_EL(mlu->lu, i, k);
            for (int t = 0; t < b->ncols; t++)
                MATD_EL(x, i, t) += MATD_EL(x, k, t) * LUik;
        }
    }

    // solve Ux = y
    for (int k = mlu->lu->ncols-1; k >= 0; k--) {
        double LUkk = 1.0 / MATD_EL(mlu->lu, k, k);
        for (int t = 0; t < b->ncols; t++)
            MATD_EL(x, k, t) *= LUkk;

        for (int i = 0; i < k; i++) {
            double LUik = -MATD_EL(mlu->lu, i, k);
            for (int t = 0; t < b->ncols; t++)
                MATD_EL(x, i, t) += MATD_EL(x, k, t) *LUik;
        }
    }

    return x;
}

matd_t *matd_solve(matd_t *A, matd_t *b)
{
    matd_plu_t *mlu = matd_plu(A);
    matd_t *x = matd_plu_solve(mlu, b);

    matd_plu_destroy(mlu);
    return x;
}

#if 0

static int randi()
{
    int v = random()&31;
    v -= 15;
    return v;
}

static double randf()
{
    double v = 1.0 *random() / RAND_MAX;
    return 2*v - 1;
}

int main(int argc, char *argv[])
{
    if (1) {
        int maxdim = 16;
        matd_t *A = matd_create(maxdim, maxdim);

        for (int iter = 0; 1; iter++) {
            srand(iter);

            if (iter % 1000 == 0)
                printf("%d\n", iter);

            int m = 1 + (random()%(maxdim-1));
            int n = 1 + (random()%(maxdim-1));

            for (int i = 0; i < m*n; i++)
                A->data[i] = randi();

            A->nrows = m;
            A->ncols = n;

//            printf("%d %d ", m, n);
            matd_svd_t svd = matd_svd(A);
            matd_destroy(svd.U);
            matd_destroy(svd.S);
            matd_destroy(svd.V);

        }

/*        matd_t *A = matd_create_data(2, 5, (double[]) { 1, 5, 2, 6,
          3, 3, 0, 7,
          1, 1, 0, -2,
          4, 0, 9, 9, 2, 6, 1, 3, 2, 5, 5, 4, -1, 2, 5, 9, 8, 2 });

          matd_svd(A);
*/
        return 0;
    }


    struct svd22 s;

    srand(0);

    matd_t *A = matd_create(2, 2);
    MATD_EL(A,0,0) = 4;
    MATD_EL(A,0,1) = 7;
    MATD_EL(A,1,0) = 2;
    MATD_EL(A,1,1) = 6;

    matd_t *U = matd_create(2, 2);
    matd_t *V = matd_create(2, 2);
    matd_t *S = matd_create(2, 2);

    for (int iter = 0; 1; iter++) {
        if (iter % 100000 == 0)
            printf("%d\n", iter);

        MATD_EL(A,0,0) = randf();
        MATD_EL(A,0,1) = randf();
        MATD_EL(A,1,0) = randf();
        MATD_EL(A,1,1) = randf();

        matd_svd22_impl(A->data, &s);

        memcpy(U->data, s.U, 4*sizeof(double));
        memcpy(V->data, s.V, 4*sizeof(double));
        MATD_EL(S,0,0) = s.S[0];
        MATD_EL(S,1,1) = s.S[1];

        assert(s.S[0] >= s.S[1]);
        assert(s.S[0] >= 0);
        assert(s.S[1] >= 0);
        if (s.S[0] == 0) {
//            printf("*"); fflush(NULL);
//            printf("%15f %15f %15f %15f\n", MATD_EL(A,0,0), MATD_EL(A,0,1), MATD_EL(A,1,0), MATD_EL(A,1,1));
        }
        if (s.S[1] == 0) {
//            printf("#"); fflush(NULL);
        }

        matd_t *USV = matd_op("M*M*M'", U, S, V);

        double maxerr = 0;
        for (int i = 0; i < 4; i++)
            maxerr = fmax(maxerr, fabs(USV->data[i] - A->data[i]));

        if (0) {
            printf("------------------------------------\n");
            printf("A:\n");
            matd_print(A, "%15f");
            printf("\nUSV':\n");
            matd_print(USV, "%15f");
            printf("maxerr: %.15f\n", maxerr);
            printf("\n\n");
        }

        matd_destroy(USV);

        assert(maxerr < 0.00001);
    }
}

#endif

// XXX NGV Cholesky
/*static double *matd_cholesky_raw(double *A, int n)
  {
  double *L = (double*)calloc(n * n, sizeof(double));

  for (int i = 0; i < n; i++) {
  for (int j = 0; j < (i+1); j++) {
  double s = 0;
  for (int k = 0; k < j; k++)
  s += L[i * n + k] * L[j * n + k];
  L[i * n + j] = (i == j) ?
  sqrt(A[i * n + i] - s) :
  (1.0 / L[j * n + j] * (A[i * n + j] - s));
  }
  }

  return L;
  }

  matd_t *matd_cholesky(const matd_t *A)
  {
  assert(A->nrows == A->ncols);
  double *L_data = matd_cholesky_raw(A->data, A->nrows);
  matd_t *L = matd_create_data(A->nrows, A->ncols, L_data);
  free(L_data);
  return L;
  }*/

// NOTE: The below implementation of Cholesky is different from the one
// used in NGV.
matd_chol_t *matd_chol(matd_t *A)
{
    assert(A->nrows == A->ncols);
    int N = A->nrows;

    // make upper right
    matd_t *U = matd_copy(A);

    // don't actually need to clear lower-left... we won't touch it.
/*    for (int i = 0; i < U->nrows; i++) {
      for (int j = 0; j < i; j++) {
//            assert(MATD_EL(U, i, j) == MATD_EL(U, j, i));
MATD_EL(U, i, j) = 0;
}
}
*/
    int is_spd = 1; // (A->nrows == A->ncols);

    for (int i = 0; i < N; i++) {
        double d = MATD_EL(U, i, i);
        is_spd &= (d > 0);

        if (d < MATD_EPS)
            d = MATD_EPS;
        d = 1.0 / sqrt(d);

        for (int j = i; j < N; j++)
            MATD_EL(U, i, j) *= d;

        for (int j = i+1; j < N; j++) {
            double s = MATD_EL(U, i, j);

            if (s == 0)
                continue;

            for (int k = j; k < N; k++) {
                MATD_EL(U, j, k) -= MATD_EL(U, i, k)*s;
            }
        }
    }

    matd_chol_t *chol = calloc(1, sizeof(matd_chol_t));
    chol->is_spd = is_spd;
    chol->u = U;
    return chol;
}

void matd_chol_destroy(matd_chol_t *chol)
{
    matd_destroy(chol->u);
    free(chol);
}

// Solve: (U')x = b, U is upper triangular
void matd_ltransposetriangle_solve(matd_t *u, const TYPE *b, TYPE *x)
{
    int n = u->ncols;
    memcpy(x, b, n*sizeof(TYPE));
    for (int i = 0; i < n; i++) {
        x[i] /= MATD_EL(u, i, i);

        for (int j = i+1; j < u->ncols; j++) {
            x[j] -= x[i] * MATD_EL(u, i, j);
        }
    }
}

// Solve: Lx = b, L is lower triangular
void matd_ltriangle_solve(matd_t *L, const TYPE *b, TYPE *x)
{
    int n = L->ncols;

    for (int i = 0; i < n; i++) {
        double acc = b[i];

        for (int j = 0; j < i; j++) {
            acc -= MATD_EL(L, i, j)*x[j];
        }

        x[i] = acc / MATD_EL(L, i, i);
    }
}

// solve Ux = b, U is upper triangular
void matd_utriangle_solve(matd_t *u, const TYPE *b, TYPE *x)
{
    for (int i = u->ncols-1; i >= 0; i--) {
        double bi = b[i];

        double diag = MATD_EL(u, i, i);

        for (int j = i+1; j < u->ncols; j++)
            bi -= MATD_EL(u, i, j)*x[j];

        x[i] = bi / diag;
    }
}

matd_t *matd_chol_solve(const matd_chol_t *chol, const matd_t *b)
{
    matd_t *u = chol->u;

    matd_t *x = matd_copy(b);

    // LUx = b

    // solve Ly = b ==> (U')y = b

    for (int i = 0; i < u->nrows; i++) {
        for (int j = 0; j < i; j++) {
            // b[i] -= L[i,j]*x[j]... replicated across columns of b
            //   ==> i.e., ==>
            // b[i,k] -= L[i,j]*x[j,k]
            for (int k = 0; k < b->ncols; k++) {
                MATD_EL(x, i, k) -= MATD_EL(u, j, i)*MATD_EL(x, j, k);
            }
        }
        // x[i] = b[i] / L[i,i]
        for (int k = 0; k < b->ncols; k++) {
            MATD_EL(x, i, k) /= MATD_EL(u, i, i);
        }
    }

    // solve Ux = y
    for (int k = u->ncols-1; k >= 0; k--) {
        double LUkk = 1.0 / MATD_EL(u, k, k);
        for (int t = 0; t < b->ncols; t++)
            MATD_EL(x, k, t) *= LUkk;

        for (int i = 0; i < k; i++) {
            double LUik = -MATD_EL(u, i, k);
            for (int t = 0; t < b->ncols; t++)
                MATD_EL(x, i, t) += MATD_EL(x, k, t) *LUik;
        }
    }

    return x;
}

/*void matd_chol_solve(matd_chol_t *chol, const TYPE *b, TYPE *x)
  {
  matd_t *u = chol->u;

  TYPE y[u->ncols];
  matd_ltransposetriangle_solve(u, b, y);
  matd_utriangle_solve(u, y, x);
  }
*/
// only sensible on PSD matrices. had expected it to be faster than
// inverse via LU... for now, doesn't seem to be.
matd_t *matd_chol_inverse(matd_t *a)
{
    assert(a->nrows == a->ncols);

    matd_chol_t *chol = matd_chol(a);

    matd_t *eye = matd_identity(a->nrows);
    matd_t *inv = matd_chol_solve(chol, eye);
    matd_destroy(eye);
    matd_chol_destroy(chol);

    return inv;
}

double matd_max(matd_t *m)
{
    double d = -DBL_MAX;
    for(int x=0; x<m->nrows; x++) {
        for(int y=0; y<m->ncols; y++) {
            if(MATD_EL(m, x, y) > d)
                d = MATD_EL(m, x, y);
        }
    }

    return d;
}