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/* Linear_System class implementation (non-inline functions).
Copyright (C) 2001-2010 Roberto Bagnara <bagnara@cs.unipr.it>
Copyright (C) 2010-2012 BUGSENG srl (http://bugseng.com)
This file is part of the Parma Polyhedra Library (PPL).
The PPL 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 3 of the License, or (at your
option) any later version.
The PPL 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., 51 Franklin Street, Fifth Floor, Boston, MA 02111-1307, USA.
For the most up-to-date information see the Parma Polyhedra Library
site: http://bugseng.com/products/ppl/ . */
#include "ppl-config.h"
#include "Linear_System.defs.hh"
#include "Coefficient.defs.hh"
#include "Dense_Row.defs.hh"
#include "Bit_Matrix.defs.hh"
#include "Scalar_Products.defs.hh"
#include "swapping_sort.templates.hh"
#include <algorithm>
#include <iostream>
#include <string>
#include <deque>
namespace PPL = Parma_Polyhedra_Library;
PPL::dimension_type
PPL::Linear_System::num_lines_or_equalities() const {
PPL_ASSERT(num_pending_rows() == 0);
const Linear_System& x = *this;
dimension_type n = 0;
for (dimension_type i = num_rows(); i-- > 0; )
if (x[i].is_line_or_equality())
++n;
return n;
}
void
PPL::Linear_System::merge_rows_assign(const Linear_System& y) {
PPL_ASSERT(row_size >= y.row_size);
// Both systems have to be sorted and have no pending rows.
PPL_ASSERT(check_sorted() && y.check_sorted());
PPL_ASSERT(num_pending_rows() == 0 && y.num_pending_rows() == 0);
using std::swap;
Linear_System& x = *this;
// A temporary vector of rows...
std::vector<Dense_Row> tmp;
// ... with enough capacity not to require any reallocations.
tmp.reserve(compute_capacity(x.num_rows() + y.num_rows(), max_num_rows()));
dimension_type xi = 0;
dimension_type x_num_rows = x.num_rows();
dimension_type yi = 0;
dimension_type y_num_rows = y.num_rows();
while (xi < x_num_rows && yi < y_num_rows) {
const int comp = compare(x[xi], y[yi]);
if (comp <= 0) {
// Elements that can be taken from `x' are actually _stolen_ from `x'
swap(x[xi++], *tmp.insert(tmp.end(), Linear_Row()));
if (comp == 0)
// A duplicate element.
++yi;
}
else {
// (comp > 0)
Linear_Row copy(y[yi++], row_size, row_capacity);
swap(copy, *tmp.insert(tmp.end(), Linear_Row()));
}
}
// Insert what is left.
if (xi < x_num_rows) {
while (xi < x_num_rows)
swap(x[xi++], *tmp.insert(tmp.end(), Linear_Row()));
}
else {
while (yi < y_num_rows) {
Linear_Row copy(y[yi++], row_size, row_capacity);
swap(copy, *tmp.insert(tmp.end(), Linear_Row()));
}
}
// We get the result vector and let the old one be destroyed.
swap(tmp, rows);
// There are no pending rows.
unset_pending_rows();
PPL_ASSERT(check_sorted());
}
void
PPL::Linear_System::set_rows_topology() {
Linear_System& x = *this;
if (is_necessarily_closed())
for (dimension_type i = num_rows(); i-- > 0; )
x[i].set_necessarily_closed();
else
for (dimension_type i = num_rows(); i-- > 0; )
x[i].set_not_necessarily_closed();
}
void
PPL::Linear_System::ascii_dump(std::ostream& s) const {
// Prints the topology, the number of rows, the number of columns
// and the sorted flag. The specialized methods provided by
// Constraint_System and Generator_System take care of properly
// printing the contents of the system.
const Linear_System& x = *this;
dimension_type x_num_rows = x.num_rows();
dimension_type x_num_columns = x.num_columns();
s << "topology " << (is_necessarily_closed()
? "NECESSARILY_CLOSED"
: "NOT_NECESSARILY_CLOSED")
<< "\n"
<< x_num_rows << " x " << x_num_columns
<< (x.sorted ? "(sorted)" : "(not_sorted)")
<< "\n"
<< "index_first_pending " << x.first_pending_row()
<< "\n";
for (dimension_type i = 0; i < x_num_rows; ++i)
x[i].ascii_dump(s);
}
PPL_OUTPUT_DEFINITIONS_ASCII_ONLY(Linear_System)
bool
PPL::Linear_System::ascii_load(std::istream& s) {
std::string str;
if (!(s >> str) || str != "topology")
return false;
if (!(s >> str))
return false;
if (str == "NECESSARILY_CLOSED")
set_necessarily_closed();
else {
if (str != "NOT_NECESSARILY_CLOSED")
return false;
set_not_necessarily_closed();
}
dimension_type nrows;
dimension_type ncols;
if (!(s >> nrows))
return false;
if (!(s >> str) || str != "x")
return false;
if (!(s >> ncols))
return false;
resize_no_copy(nrows, ncols);
if (!(s >> str) || (str != "(sorted)" && str != "(not_sorted)"))
return false;
set_sorted(str == "(sorted)");
dimension_type index;
if (!(s >> str) || str != "index_first_pending")
return false;
if (!(s >> index))
return false;
set_index_first_pending_row(index);
Linear_System& x = *this;
for (dimension_type row = 0; row < nrows; ++row)
if (!x[row].ascii_load(s))
return false;
// Check invariants.
PPL_ASSERT(OK(true));
return true;
}
void
PPL::Linear_System::insert(const Linear_Row& r) {
// The added row must be strongly normalized and have the same
// topology of the system.
PPL_ASSERT(r.check_strong_normalized());
PPL_ASSERT(topology() == r.topology());
// This method is only used when the system has no pending rows.
PPL_ASSERT(num_pending_rows() == 0);
using std::swap;
const dimension_type old_num_rows = num_rows();
const dimension_type old_num_columns = num_columns();
const dimension_type r_size = r.size();
// Resize the system, if necessary.
if (r_size > old_num_columns) {
add_zero_columns(r_size - old_num_columns);
if (!is_necessarily_closed() && old_num_rows != 0)
// Move the epsilon coefficients to the last column
// (note: sorting is preserved).
swap_columns(old_num_columns - 1, r_size - 1);
add_row(r);
}
else if (r_size < old_num_columns) {
// Create a resized copy of the row.
Linear_Row tmp_row(r, old_num_columns, row_capacity);
// If needed, move the epsilon coefficient to the last position.
if (!is_necessarily_closed())
swap(tmp_row[r_size - 1], tmp_row[old_num_columns - 1]);
add_row(tmp_row);
}
else
// Here r_size == old_num_columns.
add_row(r);
// The added row was not a pending row.
PPL_ASSERT(num_pending_rows() == 0);
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::insert_pending(const Linear_Row& r) {
// The added row must be strongly normalized and have the same
// topology of the system.
PPL_ASSERT(r.check_strong_normalized());
PPL_ASSERT(topology() == r.topology());
const dimension_type old_num_rows = num_rows();
const dimension_type old_num_columns = num_columns();
const dimension_type r_size = r.size();
// Resize the system, if necessary.
if (r_size > old_num_columns) {
add_zero_columns(r_size - old_num_columns);
if (!is_necessarily_closed() && old_num_rows != 0)
// Move the epsilon coefficients to the last column
// (note: sorting is preserved).
swap_columns(old_num_columns - 1, r_size - 1);
add_pending_row(r);
}
else if (r_size < old_num_columns)
if (is_necessarily_closed() || old_num_rows == 0)
add_pending_row(Linear_Row(r, old_num_columns, row_capacity));
else {
// Create a resized copy of the row (and move the epsilon
// coefficient to its last position).
using std::swap;
Linear_Row tmp_row(r, old_num_columns, row_capacity);
swap(tmp_row[r_size - 1], tmp_row[old_num_columns - 1]);
add_pending_row(tmp_row);
}
else
// Here r_size == old_num_columns.
add_pending_row(r);
// The added row was a pending row.
PPL_ASSERT(num_pending_rows() > 0);
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::add_pending_rows(const Linear_System& y) {
Linear_System& x = *this;
PPL_ASSERT(x.row_size == y.row_size);
const dimension_type x_n_rows = x.num_rows();
const dimension_type y_n_rows = y.num_rows();
// Grow to the required size without changing sortedness.
const bool was_sorted = sorted;
add_zero_rows(y_n_rows, Linear_Row::Flags(row_topology));
sorted = was_sorted;
// Copy the rows of `y', forcing size and capacity.
for (dimension_type i = y_n_rows; i-- > 0; ) {
Dense_Row copy(y[i], x.row_size, x.row_capacity);
using std::swap;
swap(copy, x[x_n_rows+i]);
}
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::add_rows(const Linear_System& y) {
PPL_ASSERT(num_pending_rows() == 0);
// Adding no rows is a no-op.
if (y.has_no_rows())
return;
// Check if sortedness is preserved.
if (is_sorted()) {
if (!y.is_sorted() || y.num_pending_rows() > 0)
set_sorted(false);
else {
// `y' is sorted and has no pending rows.
const dimension_type n_rows = num_rows();
if (n_rows > 0)
set_sorted(compare((*this)[n_rows-1], y[0]) <= 0);
}
}
// Add the rows of `y' as if they were pending.
add_pending_rows(y);
// There are no pending_rows.
unset_pending_rows();
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::sort_rows() {
const dimension_type num_pending = num_pending_rows();
// We sort the non-pending rows only.
sort_rows(0, first_pending_row());
set_index_first_pending_row(num_rows() - num_pending);
sorted = true;
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::sort_rows(const dimension_type first_row,
const dimension_type last_row) {
PPL_ASSERT(first_row <= last_row && last_row <= num_rows());
// We cannot mix pending and non-pending rows.
PPL_ASSERT(first_row >= first_pending_row() || last_row <= first_pending_row());
// First sort without removing duplicates.
std::vector<Dense_Row>::iterator first = nth_iter(rows, first_row);
std::vector<Dense_Row>::iterator last = nth_iter(rows, last_row);
Implementation::swapping_sort(first, last, Row_Less_Than());
// Second, move duplicates to the end.
std::vector<Dense_Row>::iterator new_last
= Implementation::swapping_unique(first, last);
// Finally, remove duplicates.
rows.erase(new_last, last);
// NOTE: we cannot check all invariants of the system here,
// because the caller still has to update `index_first_pending'.
}
void
PPL::Linear_System::add_row(const Linear_Row& r) {
// The added row must be strongly normalized and have the same
// number of elements as the existing rows of the system.
PPL_ASSERT(r.check_strong_normalized());
PPL_ASSERT(r.size() == row_size);
// This method is only used when the system has no pending rows.
PPL_ASSERT(num_pending_rows() == 0);
const bool was_sorted = is_sorted();
Dense_Matrix::add_row(r);
// We update `index_first_pending', because it must be equal to
// `num_rows()'.
set_index_first_pending_row(num_rows());
if (was_sorted) {
const dimension_type nrows = num_rows();
// The added row may have caused the system to be not sorted anymore.
if (nrows > 1) {
// If the system is not empty and the inserted row is the
// greatest one, the system is set to be sorted.
// If it is not the greatest one then the system is no longer sorted.
Linear_System& x = *this;
set_sorted(compare(x[nrows-2], x[nrows-1]) <= 0);
}
else
// A system having only one row is sorted.
set_sorted(true);
}
// The added row was not a pending row.
PPL_ASSERT(num_pending_rows() == 0);
// Do not check for strong normalization, because no modification of
// rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::add_pending_row(const Linear_Row& r) {
// The added row must be strongly normalized and have the same
// number of elements of the existing rows of the system.
PPL_ASSERT(r.check_strong_normalized());
PPL_ASSERT(r.size() == row_size);
using std::swap;
const dimension_type new_rows_size = rows.size() + 1;
if (rows.capacity() < new_rows_size) {
// Reallocation will take place.
std::vector<Dense_Row> new_rows;
new_rows.reserve(compute_capacity(new_rows_size, max_num_rows()));
new_rows.insert(new_rows.end(), new_rows_size, Dense_Row());
// Put the new row in place.
Dense_Row new_row(r, row_capacity);
dimension_type i = new_rows_size-1;
swap(new_rows[i], new_row);
// Steal the old rows.
while (i-- > 0)
new_rows[i].m_swap(rows[i]);
// Put the new rows into place.
swap(rows, new_rows);
}
else {
// Reallocation will NOT take place.
// Inserts a new empty row at the end, then substitutes it with a
// copy of the given row.
Dense_Row tmp(r, row_capacity);
swap(*rows.insert(rows.end(), Dense_Row()), tmp);
}
// The added row was a pending row.
PPL_ASSERT(num_pending_rows() > 0);
// Do not check for strong normalization, because no modification of
// rows has occurred.
PPL_ASSERT(OK(false));
}
void
PPL::Linear_System::add_pending_row(const Linear_Row::Flags flags) {
using std::swap;
const dimension_type new_rows_size = rows.size() + 1;
if (rows.capacity() < new_rows_size) {
// Reallocation will take place.
std::vector<Dense_Row> new_rows;
new_rows.reserve(compute_capacity(new_rows_size, max_num_rows()));
new_rows.insert(new_rows.end(), new_rows_size, Dense_Row());
// Put the new row in place.
Linear_Row new_row(row_size, row_capacity, flags);
dimension_type i = new_rows_size-1;
swap(new_rows[i], new_row);
// Steal the old rows.
while (i-- > 0)
new_rows[i].m_swap(rows[i]);
// Put the new vector into place.
swap(rows, new_rows);
}
else {
// Reallocation will NOT take place.
// Insert a new empty row at the end, then construct it assigning
// it the given type.
Dense_Row& new_row = *rows.insert(rows.end(), Dense_Row(flags));
static_cast<Linear_Row&>(new_row).resize(row_size, row_capacity);
}
// The added row was a pending row.
PPL_ASSERT(num_pending_rows() > 0);
}
void
PPL::Linear_System::normalize() {
Linear_System& x = *this;
const dimension_type nrows = x.num_rows();
// We normalize also the pending rows.
for (dimension_type i = nrows; i-- > 0; )
x[i].normalize();
set_sorted(nrows <= 1);
}
void
PPL::Linear_System::strong_normalize() {
Linear_System& x = *this;
const dimension_type nrows = x.num_rows();
// We strongly normalize also the pending rows.
for (dimension_type i = nrows; i-- > 0; )
x[i].strong_normalize();
set_sorted(nrows <= 1);
}
void
PPL::Linear_System::sign_normalize() {
Linear_System& x = *this;
const dimension_type nrows = x.num_rows();
// We sign-normalize also the pending rows.
for (dimension_type i = num_rows(); i-- > 0; )
x[i].sign_normalize();
set_sorted(nrows <= 1);
}
/*! \relates Parma_Polyhedra_Library::Linear_System */
bool
PPL::operator==(const Linear_System& x, const Linear_System& y) {
if (x.num_columns() != y.num_columns())
return false;
const dimension_type x_num_rows = x.num_rows();
const dimension_type y_num_rows = y.num_rows();
if (x_num_rows != y_num_rows)
return false;
if (x.first_pending_row() != y.first_pending_row())
return false;
// Notice that calling operator==(const Dense_Matrix&, const Dense_Matrix&)
// would be wrong here, as equality of the type fields would
// not be checked.
for (dimension_type i = x_num_rows; i-- > 0; )
if (x[i] != y[i])
return false;
return true;
}
void
PPL::Linear_System::sort_and_remove_with_sat(Bit_Matrix& sat) {
Linear_System& sys = *this;
// We can only sort the non-pending part of the system.
PPL_ASSERT(sys.first_pending_row() == sat.num_rows());
if (sys.first_pending_row() <= 1) {
sys.set_sorted(true);
return;
}
// First, sort `sys' (keeping `sat' consistent) without removing duplicates.
With_Bit_Matrix_iterator first(sys.rows.begin(), sat.rows.begin());
typedef With_Bit_Matrix_iterator::difference_type diff_type;
With_Bit_Matrix_iterator last
= first + static_cast<diff_type>(sat.num_rows());
Implementation::swapping_sort(first, last, Row_Less_Than());
// Second, move duplicates in `sys' to the end (keeping `sat' consistent).
With_Bit_Matrix_iterator new_last
= Implementation::swapping_unique(first, last);
const diff_type dist = last - new_last;
PPL_ASSERT(dist >= 0);
const dimension_type num_duplicates = static_cast<dimension_type>(dist);
const dimension_type new_first_pending_row
= sys.first_pending_row() - num_duplicates;
if (sys.num_pending_rows() > 0) {
// In this case, we must put the duplicates after the pending rows.
using std::swap;
const dimension_type n_rows = sys.num_rows() - 1;
for (dimension_type i = 0; i < num_duplicates; ++i)
swap(sys[new_first_pending_row + i], sys[n_rows - i]);
}
// Erasing the duplicated rows...
sys.remove_trailing_rows(num_duplicates);
sys.set_index_first_pending_row(new_first_pending_row);
// ... and the corresponding rows of the saturation matrix.
sat.remove_trailing_rows(num_duplicates);
PPL_ASSERT(sys.check_sorted());
// Now the system is sorted.
sys.set_sorted(true);
}
PPL::dimension_type
PPL::Linear_System::gauss(const dimension_type n_lines_or_equalities) {
Linear_System& x = *this;
// This method is only applied to a well-formed linear system
// having no pending rows and exactly `n_lines_or_equalities'
// lines or equalities, all of which occur before the rays or points
// or inequalities.
PPL_ASSERT(x.OK(true));
PPL_ASSERT(x.num_pending_rows() == 0);
PPL_ASSERT(n_lines_or_equalities == x.num_lines_or_equalities());
#ifndef NDEBUG
for (dimension_type i = n_lines_or_equalities; i-- > 0; )
PPL_ASSERT(x[i].is_line_or_equality());
#endif
dimension_type rank = 0;
// Will keep track of the variations on the system of equalities.
bool changed = false;
for (dimension_type j = x.num_columns(); j-- > 0; )
for (dimension_type i = rank; i < n_lines_or_equalities; ++i) {
// Search for the first row having a non-zero coefficient
// (the pivot) in the j-th column.
if (x[i][j] == 0)
continue;
// Pivot found: if needed, swap rows so that this one becomes
// the rank-th row in the linear system.
if (i > rank) {
using std::swap;
swap(x[i], x[rank]);
// After swapping the system is no longer sorted.
changed = true;
}
// Combine the row containing the pivot with all the lines or
// equalities following it, so that all the elements on the j-th
// column in these rows become 0.
for (dimension_type k = i + 1; k < n_lines_or_equalities; ++k)
if (x[k][j] != 0) {
x[k].linear_combine(x[rank], j);
changed = true;
}
// Already dealt with the rank-th row.
++rank;
// Consider another column index `j'.
break;
}
if (changed)
x.set_sorted(false);
// A well-formed system is returned.
PPL_ASSERT(x.OK(true));
return rank;
}
void
PPL::Linear_System
::back_substitute(const dimension_type n_lines_or_equalities) {
Linear_System& x = *this;
// This method is only applied to a well-formed system
// having no pending rows and exactly `n_lines_or_equalities'
// lines or equalities, all of which occur before the first ray
// or point or inequality.
PPL_ASSERT(x.OK(true));
PPL_ASSERT(x.num_columns() >= 1);
PPL_ASSERT(x.num_pending_rows() == 0);
PPL_ASSERT(n_lines_or_equalities <= x.num_lines_or_equalities());
#ifndef NDEBUG
for (dimension_type i = n_lines_or_equalities; i-- > 0; )
PPL_ASSERT(x[i].is_line_or_equality());
#endif
const dimension_type nrows = x.num_rows();
const dimension_type ncols = x.num_columns();
// Trying to keep sortedness.
bool still_sorted = x.is_sorted();
// This deque of Booleans will be used to flag those rows that,
// before exiting, need to be re-checked for sortedness.
std::deque<bool> check_for_sortedness;
if (still_sorted)
check_for_sortedness.insert(check_for_sortedness.end(), nrows, false);
for (dimension_type k = n_lines_or_equalities; k-- > 0; ) {
// For each line or equality, starting from the last one,
// looks for the last non-zero element.
// `j' will be the index of such a element.
Linear_Row& x_k = x[k];
dimension_type j = ncols - 1;
while (j != 0 && x_k[j] == 0)
--j;
// Go through the equalities above `x_k'.
for (dimension_type i = k; i-- > 0; ) {
Linear_Row& x_i = x[i];
if (x_i[j] != 0) {
// Combine linearly `x_i' with `x_k'
// so that `x_i[j]' becomes zero.
x_i.linear_combine(x_k, j);
if (still_sorted) {
// Trying to keep sortedness: remember which rows
// have to be re-checked for sortedness at the end.
if (i > 0)
check_for_sortedness[i-1] = true;
check_for_sortedness[i] = true;
}
}
}
// Due to strong normalization during previous iterations,
// the pivot coefficient `x_k[j]' may now be negative.
// Since an inequality (or ray or point) cannot be multiplied
// by a negative factor, the coefficient of the pivot must be
// forced to be positive.
const bool have_to_negate = (x_k[j] < 0);
if (have_to_negate)
for (dimension_type h = ncols; h-- > 0; )
PPL::neg_assign(x_k[h]);
// Note: we do not mark index `k' in `check_for_sortedness',
// because we will later negate back the row.
// Go through all the other rows of the system.
for (dimension_type i = n_lines_or_equalities; i < nrows; ++i) {
Linear_Row& x_i = x[i];
if (x_i[j] != 0) {
// Combine linearly the `x_i' with `x_k'
// so that `x_i[j]' becomes zero.
x_i.linear_combine(x_k, j);
if (still_sorted) {
// Trying to keep sortedness: remember which rows
// have to be re-checked for sortedness at the end.
if (i > n_lines_or_equalities)
check_for_sortedness[i-1] = true;
check_for_sortedness[i] = true;
}
}
}
if (have_to_negate)
// Negate `x_k' to restore strong-normalization.
for (dimension_type h = ncols; h-- > 0; )
PPL::neg_assign(x_k[h]);
}
// Trying to keep sortedness.
for (dimension_type i = 0; still_sorted && i+1 < nrows; ++i)
if (check_for_sortedness[i])
// Have to check sortedness of `x[i]' with respect to `x[i+1]'.
still_sorted = (compare(x[i], x[i+1]) <= 0);
// Set the sortedness flag.
x.set_sorted(still_sorted);
// A well-formed system is returned.
PPL_ASSERT(x.OK(true));
}
void
PPL::Linear_System::simplify() {
Linear_System& x = *this;
// This method is only applied to a well-formed system
// having no pending rows.
PPL_ASSERT(x.OK(true));
PPL_ASSERT(x.num_pending_rows() == 0);
// Partially sort the linear system so that all lines/equalities come first.
const dimension_type old_nrows = x.num_rows();
dimension_type nrows = old_nrows;
dimension_type n_lines_or_equalities = 0;
for (dimension_type i = 0; i < nrows; ++i)
if (x[i].is_line_or_equality()) {
if (n_lines_or_equalities < i) {
using std::swap;
swap(x[i], x[n_lines_or_equalities]);
// The system was not sorted.
PPL_ASSERT(!x.sorted);
}
++n_lines_or_equalities;
}
// Apply Gaussian elimination to the subsystem of lines/equalities.
const dimension_type rank = x.gauss(n_lines_or_equalities);
// Eliminate any redundant line/equality that has been detected.
if (rank < n_lines_or_equalities) {
const dimension_type
n_rays_or_points_or_inequalities = nrows - n_lines_or_equalities;
const dimension_type
num_swaps = std::min(n_lines_or_equalities - rank,
n_rays_or_points_or_inequalities);
using std::swap;
for (dimension_type i = num_swaps; i-- > 0; )
swap(x[--nrows], x[rank + i]);
x.remove_trailing_rows(old_nrows - nrows);
x.unset_pending_rows();
if (n_rays_or_points_or_inequalities > num_swaps)
x.set_sorted(false);
n_lines_or_equalities = rank;
}
// Apply back-substitution to the system of rays/points/inequalities.
x.back_substitute(n_lines_or_equalities);
// A well-formed system is returned.
PPL_ASSERT(x.OK(true));
}
void
PPL::Linear_System::add_rows_and_columns(const dimension_type n) {
PPL_ASSERT(n > 0);
const bool was_sorted = is_sorted();
const dimension_type old_n_rows = num_rows();
const dimension_type old_n_columns = num_columns();
add_zero_rows_and_columns(n, n, Linear_Row::Flags(row_topology));
Linear_System& x = *this;
// The old system is moved to the bottom.
using std::swap;
for (dimension_type i = old_n_rows; i-- > 0; )
swap(x[i], x[i + n]);
for (dimension_type i = n, c = old_n_columns; i-- > 0; ) {
// The top right-hand sub-system (i.e., the system made of new
// rows and columns) is set to the specular image of the identity
// matrix.
Linear_Row& r = x[i];
r[c++] = 1;
r.set_is_line_or_equality();
// Note: `r' is strongly normalized.
}
// If the old system was empty, the last row added is either
// a positivity constraint or a point.
if (old_n_columns == 0) {
x[n-1].set_is_ray_or_point_or_inequality();
// Since ray, points and inequalities come after lines
// and equalities, this case implies the system is sorted.
set_sorted(true);
}
else if (was_sorted)
set_sorted(compare(x[n-1], x[n]) <= 0);
// A well-formed system has to be returned.
PPL_ASSERT(OK(true));
}
void
PPL::Linear_System::sort_pending_and_remove_duplicates() {
PPL_ASSERT(num_pending_rows() > 0);
PPL_ASSERT(is_sorted());
Linear_System& x = *this;
// The non-pending part of the system is already sorted.
// Now sorting the pending part..
const dimension_type first_pending = x.first_pending_row();
x.sort_rows(first_pending, x.num_rows());
// Recompute the number of rows, because we may have removed
// some rows occurring more than once in the pending part.
const dimension_type old_num_rows = x.num_rows();
dimension_type num_rows = old_num_rows;
dimension_type k1 = 0;
dimension_type k2 = first_pending;
dimension_type num_duplicates = 0;
// In order to erase them, put at the end of the system
// those pending rows that also occur in the non-pending part.
using std::swap;
while (k1 < first_pending && k2 < num_rows) {
const int cmp = compare(x[k1], x[k2]);
if (cmp == 0) {
// We found the same row.
++num_duplicates;
--num_rows;
// By initial sortedness, we can increment index `k1'.
++k1;
// Do not increment `k2'; instead, swap there the next pending row.
if (k2 < num_rows)
swap(x[k2], x[k2 + num_duplicates]);
}
else if (cmp < 0)
// By initial sortedness, we can increment `k1'.
++k1;
else {
// Here `cmp > 0'.
// Increment `k2' and, if we already found any duplicate,
// swap the next pending row in position `k2'.
++k2;
if (num_duplicates > 0 && k2 < num_rows)
swap(x[k2], x[k2 + num_duplicates]);
}
}
// If needed, swap any duplicates found past the pending rows
// that has not been considered yet; then erase the duplicates.
if (num_duplicates > 0) {
if (k2 < num_rows)
for (++k2; k2 < num_rows; ++k2)
swap(x[k2], x[k2 + num_duplicates]);
x.remove_trailing_rows(old_num_rows - num_rows);
}
// Do not check for strong normalization,
// because no modification of rows has occurred.
PPL_ASSERT(OK(false));
}
bool
PPL::Linear_System::check_sorted() const {
const Linear_System& x = *this;
for (dimension_type i = first_pending_row(); i-- > 1; )
if (compare(x[i], x[i-1]) < 0)
return false;
return true;
}
bool
PPL::Linear_System::OK(const bool check_strong_normalized) const {
#ifndef NDEBUG
using std::endl;
using std::cerr;
#endif
// `index_first_pending' must be less than or equal to `num_rows()'.
if (first_pending_row() > num_rows()) {
#ifndef NDEBUG
cerr << "Linear_System has a negative number of pending rows!"
<< endl;
#endif
return false;
}
// An empty system is OK,
// unless it is an NNC system with exactly one column.
if (has_no_rows()) {
if (is_necessarily_closed() || num_columns() != 1)
return true;
else {
#ifndef NDEBUG
cerr << "NNC Linear_System has one column" << endl;
#endif
return false;
}
}
// A non-empty system will contain constraints or generators; in
// both cases it must have at least one column for the inhomogeneous
// term and, if it is NNC, another one for the epsilon coefficient.
const dimension_type min_cols = is_necessarily_closed() ? 1U : 2U;
if (num_columns() < min_cols) {
#ifndef NDEBUG
cerr << "Linear_System has fewer columns than the minimum "
<< "allowed by its topology:"
<< endl
<< "num_columns is " << num_columns()
<< ", minimum is " << min_cols
<< endl;
#endif
return false;
}
const Linear_System& x = *this;
const dimension_type n_rows = num_rows();
for (dimension_type i = 0; i < n_rows; ++i) {
if (!x[i].OK(row_size, row_capacity))
return false;
// Checking for topology mismatches.
if (x.topology() != x[i].topology()) {
#ifndef NDEBUG
cerr << "Topology mismatch between the system "
<< "and one of its rows!"
<< endl;
#endif
return false;
}
}
if (check_strong_normalized) {
// Check for strong normalization of rows.
// Note: normalization cannot be checked inside the
// Linear_Row::OK() method, because a Linear_Row object may also
// implement a Linear_Expression object, which in general cannot
// be (strongly) normalized.
Linear_System tmp(x, With_Pending());
tmp.strong_normalize();
if (x != tmp) {
#ifndef NDEBUG
cerr << "Linear_System rows are not strongly normalized!"
<< endl;
#endif
return false;
}
}
if (sorted && !check_sorted()) {
#ifndef NDEBUG
cerr << "The system declares itself to be sorted but it is not!"
<< endl;
#endif
return false;
}
// All checks passed.
return true;
}
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