X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/0eb371ccd29974a375f8c6526b1ba14946997662..57d65ecd18c591154169428816b1623be700097a:/src/surf/lagrange.c diff --git a/src/surf/lagrange.c b/src/surf/lagrange.c index cfabb336ee..8530c65d31 100644 --- a/src/surf/lagrange.c +++ b/src/surf/lagrange.c @@ -17,7 +17,6 @@ #include #endif - XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)"); XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, @@ -39,7 +38,7 @@ double partial_diff_lambda(double lambda, void *param_cnst); double diff_aux(lmm_variable_t var, double x); -static int __check_kkt(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) +static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) { xbt_swag_t elem_list = NULL; lmm_element_t elem = NULL; @@ -48,7 +47,6 @@ static int __check_kkt(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) double tmp; - //verify the KKT property for each link xbt_swag_foreach(cnst, cnst_list) { tmp = 0; elem_list = &(cnst->element_set); @@ -66,21 +64,14 @@ static int __check_kkt(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) cnst, cnst->bound, tmp); return 0; } - DEBUG3("Checking KKT for constraint (%p): sat = %f, lambda = %f ", + DEBUG3("Checking feasability for constraint (%p): sat = %f, lambda = %f ", cnst, tmp - cnst->bound, cnst->lambda); - -/* if(!((fabs(tmp - cnst->bound)lambda>=MAXMIN_PRECISION) || */ -/* (fabs(tmp - cnst->bound)>=MAXMIN_PRECISION && cnst->lambdabound < 0 || var->weight <= 0) continue; - DEBUG3("Checking KKT for variable (%p): sat = %f mu = %f", var, + DEBUG3("Checking feasability for variable (%p): sat = %f mu = %f", var, var->value - var->bound, var->mu); if (double_positive(var->value - var->bound)) { @@ -90,12 +81,6 @@ static int __check_kkt(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) var, var->bound, var->value); return 0; } - -/* if(!((fabs(var->value - var->bound)mu>=MAXMIN_PRECISION) || */ -/* (fabs(var->value - var->bound)>=MAXMIN_PRECISION && var->mu epsilon_min_error && iteration < max_iterations) { + while (overall_modification > epsilon_min_error && iteration < max_iterations) { + int dual_updated=0; iteration++; DEBUG1("************** ITERATION %d **************", iteration); - + DEBUG0("-------------- Gradient Descent ----------"); /* * Compute the value of mu_i */ //forall mu_i in mu_1, mu_2, ..., mu_n xbt_swag_foreach(var, var_list) { if ((var->bound >= 0) && (var->weight > 0)) { - DEBUG1("====> Working on var (%p)", var); + DEBUG1("Working on var (%p)", var); var->new_mu = dichotomy(var->mu, partial_diff_mu, var, dichotomy_min_error); - if (var->new_mu < 0) - var->new_mu = 0; - DEBUG3("====> var->mu (%p) : %g -> %g", var, var->mu, var->new_mu); + dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); + DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); + DEBUG3("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu); var->mu = var->new_mu; } } @@ -197,11 +183,13 @@ void lagrange_solve(lmm_system_t sys) */ //forall lambda_i in lambda_1, lambda_2, ..., lambda_n xbt_swag_foreach(cnst, cnst_list) { - DEBUG1("====> Working on cnst (%p)", cnst); + DEBUG1("Working on cnst (%p)", cnst); cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error); - DEBUG2("====> cnst->lambda (%p) = %e", cnst, cnst->new_lambda); + dual_updated += (fabs(cnst->new_lambda-cnst->lambda)>dichotomy_min_error); + DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); + DEBUG3("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda); cnst->lambda = cnst->new_lambda; } @@ -209,7 +197,8 @@ void lagrange_solve(lmm_system_t sys) * Now computes the values of each variable (\rho) based on * the values of \lambda and \mu. */ - overall_error = 0; + DEBUG0("-------------- Check convergence ----------"); + overall_modification = 0; xbt_swag_foreach(var, var_list) { if (var->weight <= 0) var->value = 0.0; @@ -227,30 +216,33 @@ void lagrange_solve(lmm_system_t sys) //uses the partial differential inverse function tmp = var->func_fpi(var, tmp); - //computes de overall_error using normalized value - if (overall_error < (fabs(var->value - tmp) / tmp)) { - overall_error = (fabs(var->value - tmp) / tmp); + if (overall_modification < (fabs(var->value - tmp)/tmp)) { + overall_modification = (fabs(var->value - tmp)/tmp); } var->value = tmp; + DEBUG3("New value of var (%p) = %e, overall_modification = %e", var, + var->value, overall_modification); } - DEBUG3("======> value of var (%p) = %e, overall_error = %e", var, - var->value, overall_error); } - if (!__check_kkt(cnst_list, var_list, 0)) - overall_error = 1.0; - DEBUG2("Iteration %d: Overall_error : %f", iteration, overall_error); + if (!__check_feasible(cnst_list, var_list, 0)) + overall_modification = 1.0; + DEBUG2("Iteration %d: overall_modification : %f", iteration, overall_modification); + if(!dual_updated) { + WARN1("Could not improve the convergence at iteration %d. Drop it!",iteration); + break; + } } - __check_kkt(cnst_list, var_list, 1); + __check_feasible(cnst_list, var_list, 1); - if (overall_error <= epsilon_min_error) { + if (overall_modification <= epsilon_min_error) { DEBUG1("The method converges in %d iterations.", iteration); } if (iteration >= max_iterations) { - WARN1 + DEBUG1 ("Method reach %d iterations, which is the maximum number of iterations allowed.", iteration); } @@ -293,67 +285,76 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, overall_error = 1; if ((diff_0 = diff(1e-16, var_cnst)) >= 0) { - CDEBUG1(surf_lagrange_dichotomy, "====> returning 0.0 (diff = %e)", + CDEBUG1(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0); + XBT_OUT; return 0.0; } - CDEBUG1(surf_lagrange_dichotomy, - "====> not detected positive diff in 0 (%e)", diff_0); + min_diff = diff(min, var_cnst); + max_diff = diff(max, var_cnst); while (overall_error > min_error) { - - min_diff = diff(min, var_cnst); - max_diff = diff(max, var_cnst); - - CDEBUG2(surf_lagrange_dichotomy, - "DICHOTOMY ===> min = %1.20f , max = %1.20f", min, max); - CDEBUG2(surf_lagrange_dichotomy, - "DICHOTOMY ===> diffmin = %1.20f , diffmax = %1.20f", min_diff, - max_diff); + CDEBUG4(surf_lagrange_dichotomy, + "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f", min, max, + min_diff,max_diff); if (min_diff > 0 && max_diff > 0) { if (min == max) { CDEBUG0(surf_lagrange_dichotomy, "Decreasing min"); min = min / 2.0; + min_diff = diff(min, var_cnst); } else { CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = min; + max_diff = min_diff; + } } else if (min_diff < 0 && max_diff < 0) { if (min == max) { CDEBUG0(surf_lagrange_dichotomy, "Increasing max"); max = max * 2.0; + max_diff = diff(max, var_cnst); } else { CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = max; + min_diff = max_diff; } } else if (min_diff < 0 && max_diff > 0) { middle = (max + min) / 2.0; - middle_diff = diff(middle, var_cnst); + CDEBUG1(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",middle); - if (max != 0.0 && min != 0.0) { - overall_error = fabs(min - max) / max; + if((min==middle) || (max==middle)) { + CWARN2(surf_lagrange_dichotomy,"Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f." + " Reaching the 'double' limits. Maybe scaling your function would help.", + min, max-min); + break; } + middle_diff = diff(middle, var_cnst); if (middle_diff < 0) { + CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = middle; + min_diff = middle_diff; + overall_error = max-middle_diff; } else if (middle_diff > 0) { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = middle; + max_diff = middle_diff; + overall_error = max-middle_diff; } else { - CWARN0(surf_lagrange_dichotomy, - "Found an optimal solution with 0 error!"); overall_error = 0; - return middle; } - } else if (min_diff == 0) { - return min; + max=min; + overall_error = 0; } else if (max_diff == 0) { - return max; + min=max; + overall_error = 0; } else if (min_diff > 0 && max_diff < 0) { CWARN0(surf_lagrange_dichotomy, "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); + abort(); } else { CWARN2(surf_lagrange_dichotomy, "diffmin (%1.20f) or diffmax (%1.20f) are something I don't know, taking no action.", @@ -362,10 +363,8 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, } } + CDEBUG1(surf_lagrange_dichotomy, "returning %e", (min + max) / 2.0); XBT_OUT; - - CDEBUG1(surf_lagrange_dichotomy, "====> returning %e", - (min + max) / 2.0); return ((min + max) / 2.0); } @@ -442,9 +441,6 @@ double partial_diff_lambda(double lambda, void *param_cnst) lambda_partial += cnst->bound; - - CDEBUG1(surf_lagrange_dichotomy, "returning = %1.20f", lambda_partial); - XBT_OUT; return lambda_partial; } @@ -455,13 +451,59 @@ double diff_aux(lmm_variable_t var, double x) double tmp_fpi, result; XBT_IN2("(var (%p), x (%1.20f))", var, x); - xbt_assert0(var->func_fp, + xbt_assert0(var->func_fpi, "Initialize the protocol functions first create variables before."); tmp_fpi = var->func_fpi(var, x); result = - tmp_fpi; - CDEBUG1(surf_lagrange_dichotomy, "returning %1.20f", result); XBT_OUT; return result; } + +/** \brief Attribute the value bound to var->bound. + * + * \param func_fpi inverse of the partial differential of f (f prime inverse, (f')^{-1}) + * + * Set default functions to the ones passed as parameters. This is a polimorfism in C pure, enjoy the roots of programming. + * + */ +void lmm_set_default_protocol_function(double (* func_fpi) (lmm_variable_t var, double x)) +{ + func_fpi_def = func_fpi; +} + + +/**************** Vegas and Reno functions *************************/ +/* + * NOTE for Reno: all functions consider the network + * coeficient (alpha) equal to 1. + */ + +/* + * For Vegas: $f(x) = \alpha D_f\ln(x)$ + * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$ + */ +#define VEGAS_SCALING 1000.0 +double func_vegas_fpi(lmm_variable_t var, double x){ + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + return VEGAS_SCALING*var->df/x; +} + +/* + * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$ + * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ + */ +#define RENO_SCALING 1000.0 +double func_reno_fpi(lmm_variable_t var, double x){ + double res_fpi; + + xbt_assert0(var->df>0.0,"Don't call me with stupid values!"); + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + + res_fpi = 1/(var->df*var->df*x) - 2/(3*var->df*var->df); + if(res_fpi<=0.0) return 0.0; +/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */ + return sqrt(RENO_SCALING*res_fpi); +} +