void HardMatching::normalize ()
{
optimize_1();
for (size_t i = 0; i < size_1(); ++i) {
m_1_non[i] = true;
}
for (size_t ij = 0; ij < size_arc(); ++ij) {
Arc arc = m_arcs[ij];
m_1_ass[ij] = (m_post_ass[ij] == m_optima[arc.i]);
}
optimize_2();
for (size_t j = 0; j < size_2(); ++j) {
m_2_non[j] = true;
}
for (size_t ij = 0; ij < size_arc(); ++ij) {
Arc arc = m_arcs[ij];
m_2_ass[ij] = (m_post_ass[ij] == m_optima[arc.j]);
if (post_1_ass(ij) and post_2_ass(ij)) {
m_1_ass[ij] = true;
m_1_non[arc.i] = false;
m_2_non[arc.j] = false;
} else {
m_1_ass[ij] = false;
}
}
}
void HardMatching::propagate_12 ()
{
optimize_1();
for (size_t ij = 0; ij < size_arc(); ++ij) {
Arc arc = m_arcs[ij];
float message = m_message_12[ij]
= -m_optima[arc.i].best_alternative(m_post_ass[ij]);
m_post_ass[ij] = message
+ m_message_21[ij]
+ m_prior_ass[ij];
ASSERT1_COST(m_post_ass[ij]);
}
}
/*-------------------------------------------------------------------------*/
static void
optimize(curr_poly_t *c, poly_stage2_t *data,
root_sieve_t *rs, assess_t *assess,
stage2_stat_t *stats)
{
int err;
double skewness;
double pol_norm;
double alpha_proj;
double log_max_norm_2 = log(data->max_norm_2);
profile_start(PROF_ALL);
while (1) {
err = read_a5pd(c, data);
if (err < 0) {
if (feof(data->infile))
break;
continue;
}
if (!pol_expand(c, data->gmp_N)) {
mpz_out_str(stdout, 10, c->gmp_a[5]);
fprintf(stderr, "expand failed\n");
continue;
}
profile_start(PROF_INITIAL_OPTIMIZE);
optimize_1(c, &skewness, &pol_norm, &alpha_proj);
profile_stop(PROF_INITIAL_OPTIMIZE);
if (pol_norm * exp(alpha_proj) > data->max_norm_1)
continue;
root_sieve_run(c, log_max_norm_2, data, rs, assess,
stats, skewness, pol_norm, alpha_proj);
}
profile_stop(PROF_ALL);
}
