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/** @file check_matrices.cpp
*
* Here we test manipulations on GiNaC's symbolic matrices. */
/*
* GiNaC Copyright (C) 1999-2002 Johannes Gutenberg University Mainz, Germany
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
#include "checks.h"
/* determinants of some sparse symbolic matrices with coefficients in
* an integral domain. */
static unsigned integdom_matrix_determinants(void)
{
unsigned result = 0;
symbol a("a");
for (unsigned size=3; size<20; ++size) {
matrix A(size,size);
// populate one element in each row:
for (unsigned r=0; r<size-1; ++r)
A.set(r,unsigned(rand()%size),dense_univariate_poly(a,5));
// set the last row to a linear combination of two other lines
// to guarantee that the determinant is zero:
for (unsigned c=0; c<size; ++c)
A.set(size-1,c,A(0,c)-A(size-2,c));
if (!A.determinant().is_zero()) {
clog << "Determinant of " << size << "x" << size << " matrix "
<< endl << A << endl
<< "was not found to vanish!" << endl;
++result;
}
}
return result;
}
/* determinants of some symbolic matrices with multivariate rational function
* coefficients. */
static unsigned rational_matrix_determinants(void)
{
unsigned result = 0;
symbol a("a"), b("b"), c("c");
for (unsigned size=3; size<8; ++size) {
matrix A(size,size);
for (unsigned r=0; r<size-1; ++r) {
// populate one or two elements in each row:
for (unsigned ec=0; ec<2; ++ec) {
ex numer = sparse_tree(a, b, c, 1+rand()%4, false, false, false);
ex denom;
do {
denom = sparse_tree(a, b, c, rand()%2, false, false, false);
} while (denom.is_zero());
A.set(r,unsigned(rand()%size),numer/denom);
}
}
// set the last row to a linear combination of two other lines
// to guarantee that the determinant is zero:
for (unsigned co=0; co<size; ++co)
A.set(size-1,co,A(0,co)-A(size-2,co));
if (!A.determinant().is_zero()) {
clog << "Determinant of " << size << "x" << size << " matrix "
<< endl << A << endl
<< "was not found to vanish!" << endl;
++result;
}
}
return result;
}
/* Some quite funny determinants with functions and stuff like that inside. */
static unsigned funny_matrix_determinants(void)
{
unsigned result = 0;
symbol a("a"), b("b"), c("c");
for (unsigned size=3; size<7; ++size) {
matrix A(size,size);
for (unsigned co=0; co<size-1; ++co) {
// populate one or two elements in each row:
for (unsigned ec=0; ec<2; ++ec) {
ex numer = sparse_tree(a, b, c, 1+rand()%3, true, true, false);
ex denom;
do {
denom = sparse_tree(a, b, c, rand()%2, false, true, false);
} while (denom.is_zero());
A.set(unsigned(rand()%size),co,numer/denom);
}
}
// set the last column to a linear combination of two other columns
// to guarantee that the determinant is zero:
for (unsigned ro=0; ro<size; ++ro)
A.set(ro,size-1,A(ro,0)-A(ro,size-2));
if (!A.determinant().is_zero()) {
clog << "Determinant of " << size << "x" << size << " matrix "
<< endl << A << endl
<< "was not found to vanish!" << endl;
++result;
}
}
return result;
}
/* compare results from different determinant algorithms.*/
static unsigned compare_matrix_determinants(void)
{
unsigned result = 0;
symbol a("a");
for (unsigned size=2; size<7; ++size) {
matrix A(size,size);
for (unsigned co=0; co<size; ++co) {
for (unsigned ro=0; ro<size; ++ro) {
// populate some elements
ex elem = 0;
if (rand()%(size/2) == 0)
elem = sparse_tree(a, a, a, rand()%3, false, true, false);
A.set(ro,co,elem);
}
}
ex det_gauss = A.determinant(determinant_algo::gauss);
ex det_laplace = A.determinant(determinant_algo::laplace);
ex det_divfree = A.determinant(determinant_algo::divfree);
ex det_bareiss = A.determinant(determinant_algo::bareiss);
if ((det_gauss-det_laplace).normal() != 0 ||
(det_bareiss-det_laplace).normal() != 0 ||
(det_divfree-det_laplace).normal() != 0) {
clog << "Determinant of " << size << "x" << size << " matrix "
<< endl << A << endl
<< "is inconsistent between different algorithms:" << endl
<< "Gauss elimination: " << det_gauss << endl
<< "Minor elimination: " << det_laplace << endl
<< "Division-free elim.: " << det_divfree << endl
<< "Fraction-free elim.: " << det_bareiss << endl;
++result;
}
}
return result;
}
static unsigned symbolic_matrix_inverse(void)
{
unsigned result = 0;
symbol a("a"), b("b"), c("c");
for (unsigned size=2; size<5; ++size) {
matrix A(size,size);
do {
for (unsigned co=0; co<size; ++co) {
for (unsigned ro=0; ro<size; ++ro) {
// populate some elements
ex elem = 0;
if (rand()%(size/2) == 0)
elem = sparse_tree(a, b, c, rand()%2, false, true, false);
A.set(ro,co,elem);
}
}
} while (A.determinant() == 0);
matrix B = A.inverse();
matrix C = A.mul(B);
bool ok = true;
for (unsigned ro=0; ro<size; ++ro)
for (unsigned co=0; co<size; ++co)
if (C(ro,co).normal() != (ro==co?1:0))
ok = false;
if (!ok) {
clog << "Inverse of " << size << "x" << size << " matrix "
<< endl << A << endl
<< "erroneously returned: "
<< endl << B << endl;
++result;
}
}
return result;
}
unsigned check_matrices(void)
{
unsigned result = 0;
cout << "checking symbolic matrix manipulations" << flush;
clog << "---------symbolic matrix manipulations:" << endl;
result += integdom_matrix_determinants(); cout << '.' << flush;
result += rational_matrix_determinants(); cout << '.' << flush;
result += funny_matrix_determinants(); cout << '.' << flush;
result += compare_matrix_determinants(); cout << '.' << flush;
result += symbolic_matrix_inverse(); cout << '.' << flush;
if (!result) {
cout << " passed " << endl;
clog << "(no output)" << endl;
} else {
cout << " failed " << endl;
}
return result;
}
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