Module: ppl/ppl Branch: master Commit: 92d3b0df6261f177871ee667186d2c73bb1141d5 URL: http://www.cs.unipr.it/git/gitweb.cgi?p=ppl/ppl.git;a=commit;h=92d3b0df6261f...
Author: Roberto Bagnara bagnara@cs.unipr.it Date: Fri Feb 24 11:02:30 2012 +0100
Delimit pseudo-code in non-Doxygen comments within <CODE> </CODE>. Comments improved.
---
src/MIP_Problem.cc | 11 +++++++---- src/Octagonal_Shape.templates.hh | 11 +++++++---- src/PIP_Tree.cc | 6 ++++-- 3 files changed, 18 insertions(+), 10 deletions(-)
diff --git a/src/MIP_Problem.cc b/src/MIP_Problem.cc index 83eeeb8..9eb8022 100644 --- a/src/MIP_Problem.cc +++ b/src/MIP_Problem.cc @@ -1685,10 +1685,13 @@ PPL::MIP_Problem::erase_artificials(const dimension_type begin_artificials, // ... then properly set the element in the (new) last column, // encoding the kind of optimization; ... { - // This block is equivalent to: - // working_cost[new_last_column] = working_cost.get(old_last_column); - // But it avoids storing zeroes. - + // This block is equivalent to + // + // <CODE> + // working_cost[new_last_column] = working_cost.get(old_last_column); + // </CODE> + // + // but it avoids storing zeroes. Coefficient_traits::const_reference old_cost = working_cost.get(old_last_column); if (old_cost == 0) diff --git a/src/Octagonal_Shape.templates.hh b/src/Octagonal_Shape.templates.hh index 1e4a0b8..b029054 100644 --- a/src/Octagonal_Shape.templates.hh +++ b/src/Octagonal_Shape.templates.hh @@ -1305,7 +1305,7 @@ Octagonal_Shape<T>::frequency(const Linear_Expression& expr, // and return true. Otherwise the values for \p expr // are not discrete so return false.
- // Space dimension = 0: if empty, then return false; + // Space dimension is 0: if empty, then return false; // otherwise the frequency is 0 and the value is the inhomogeneous term. if (space_dim == 0) { if (is_empty()) @@ -2447,9 +2447,12 @@ Octagonal_Shape<T>::strong_closure_assign() const { // Unfolding two iterations on `j': this ensures that // the loop exit condition `j <= i' is OK. for (dimension_type j = 0; j <= i; ) { - // First iteration: - // sum1 = x_i_k + x_k_j == x_ck_ci + x_k_j; - // sum2 = x_i_ck + x_ck_j == x_k_ci + x_ck_j. + // First iteration: compute + // + // <CODE> + // sum1 = x_i_k + x_k_j == x_ck_ci + x_k_j; + // sum2 = x_i_ck + x_ck_j == x_k_ci + x_ck_j; + // </CODE> add_assign_r(sum1, vec_ck_ci, vec_k[j], ROUND_UP); add_assign_r(sum2, vec_k_ci, vec_ck[j], ROUND_UP); min_assign(sum1, sum2); diff --git a/src/PIP_Tree.cc b/src/PIP_Tree.cc index b091292..b7dbbb2 100644 --- a/src/PIP_Tree.cc +++ b/src/PIP_Tree.cc @@ -257,7 +257,8 @@ column_lower(const Matrix& tableau, const dimension_type num_vars = mapping.size(); dimension_type k = 0; // While loop guard is: (k < num_rows && lhs == rhs). - // Return value is false, if k >= num_rows; lhs < rhs, otherwise. + // Return value is false, if k >= num_rows; it is equivalent to + // lhs < rhs, otherwise. // Try to optimize the computation of lhs and rhs. while (true) { const dimension_type mk = mapping[k]; @@ -282,7 +283,8 @@ column_lower(const Matrix& tableau, } // Optimizing for: lhs == 0 && rhs == 0; continue; - } else { + } + else { // Not in base. const Row& t_mk = tableau[mk]; Coefficient_traits::const_reference t_mk_ja = t_mk.get(ja);