Module: ppl/ppl Branch: pip Commit: 41ead44c160d6d940db2844baae95db469a68beb URL: http://www.cs.unipr.it/git/gitweb.cgi?p=ppl/ppl.git;a=commit;h=41ead44c160d6...
Author: François Galea francois.galea@uvsq.fr Date: Mon Sep 14 17:32:25 2009 +0200
Implemented the first steps of the parametric simplex algorithm.
---
src/PIP_Tree.cc | 129 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 files changed, 127 insertions(+), 2 deletions(-)
diff --git a/src/PIP_Tree.cc b/src/PIP_Tree.cc index c43715e..173d39f 100644 --- a/src/PIP_Tree.cc +++ b/src/PIP_Tree.cc @@ -467,8 +467,133 @@ PIP_Solution_Node::update_tableau(PIP_Tree_Node ** /* parent_ref */, }
PIP_Problem_Status -PIP_Solution_Node::solve(PIP_Tree_Node** /* parent_ref */, - const Constraint_System& /* context */) { +PIP_Solution_Node::solve(PIP_Tree_Node** parent_ref, + const Matrix& ctx) { + Matrix context(ctx); + merge_assign(context, constraints_); + const dimension_type n_a_d = not_a_dimension(); + + // Main loop of the simplex algorithm + for(;;) { + dimension_type i; + dimension_type i_ = n_a_d; + dimension_type i__ = n_a_d; + dimension_type num_rows = tableau.t.num_rows(); + dimension_type num_vars = tableau.s.num_columns(); + dimension_type num_params = tableau.t.num_columns(); + Row_Sign s; + + for (i=0; i<num_rows; ++i) { + Row_Sign s = sign[i]; + if (s == UNKNOWN) { + s = row_sign(tableau.t[i]); + sign[i] = s; + } + /* Locate first row with negative parameter row */ + if (s == NEGATIVE && i_ == n_a_d) + i_ = i; + /* Locate first row with unknown-signed parameter row */ + if (s == MIXED && i__ == n_a_d) + i__ = i; + } + + /* If no negative parameter row found, try to refine the sign of + undetermined rows using compatibility checks with the current context + */ + if (i_ == n_a_d && i__ != n_a_d) { + for (i=i__; i<num_rows; ++i) { + if (sign[i] != MIXED) + continue; + s = ZERO; + if (compatibility_check(context, tableau.t[i])) + // constraint t_i(z) >= 0 is compatible with the context + s = POSITIVE; + Row c(num_params, Row::Flags()); + negate_assign(c, tableau.t[i]); + if (compatibility_check(context, c)) { + // constraint t_i(z) < 0 <=> -t_i(z)-1 >= 0 is compatible + if (s == POSITIVE) + s = MIXED; + else + s = NEGATIVE; + } + if (s == NEGATIVE && i_ == n_a_d) + // first negative row found + i_ = i; + if (s != MIXED) { + // clear first mixed-sign row index if row is found to be not mixed + if (i == i__) + i__ = n_a_d; + } else if (i__ == n_a_d) + // first mixed-sign row found + i__ = i; + sign[i] = s; + } + } + + /* If we have found a row i_ with negative parameters : + Either the problem is empty, or a pivoting step is required + */ + if (i_ != n_a_d) { +#if NOISY_PIP + std::cout << "Found row with negative parameters: " << i_ + << std::endl; +#endif + dimension_type j; + Coefficient z(0); + Coefficient sij, cij, cij_; + Coefficient c; + Row &row = tableau.s[i_]; + /* Look for a positive S_ij such as the j^th column/S_ij is + lexico-minimal + */ + dimension_type j_ = n_a_d; + for (j=0; j<num_vars; ++j) { + if (row[j] > 0) { + if (j_ == n_a_d) { + j_ = j; + sij = row[j]; + } else { + /* Determine which column (j or j_) is lexico-minimal */ + dimension_type k = 0; + do { + dimension_type mk = mapping[k]; + if (basis[k]) { + /* reconstitute the identity submatrix part of S */ + cij = (mk==j) ? tableau.s.get_denominator() : 0; + cij_ = (mk==j_) ? tableau.s.get_denominator() : 0; + } else { + cij = tableau.s[mk][j]; + cij_ = tableau.s[mk][j_]; + } + } while (k < num_vars && cij * sij == cij_ * row[j]); + if (k < num_vars && cij * sij < cij_ * row[j]) { + j_ = j; + sij = row[j]; + } + } + } + } + + /* If no positive S_ij: problem is empty */ + if (j_ == n_a_d) { +#if NOISY_PIP + std::cout << "No positive pivot found: Solution = _|_" + << std::endl; +#endif + *parent_ref = 0; + delete this; + return UNFEASIBLE_PIP_PROBLEM; + } +#if NOISY_PIP + std::cout << "Pivot column: " << j_ + << std::endl; +#endif + + } + + } // Main loop of the simplex algorithm + //FIXME return OPTIMIZED_PIP_PROBLEM; }