X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/8a76d5717862842060081d52f06c092250a9a573..6ec12164f9609473b06b985e6656a0cec77716f3:/src/surf/lagrange.c diff --git a/src/surf/lagrange.c b/src/surf/lagrange.c index 9aa0fd4a0c..40d49edab8 100644 --- a/src/surf/lagrange.c +++ b/src/surf/lagrange.c @@ -17,29 +17,31 @@ #include #endif - XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)"); -XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, +XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf_lagrange, "Logging specific to SURF (lagrange dichotomy)"); +#define SHOW_EXPR(expr) CDEBUG1(surf_lagrange,#expr " = %g",expr); + +double (* func_f_def ) (lmm_variable_t , double); +double (* func_fp_def ) (lmm_variable_t , double); +double (* func_fpi_def )(lmm_variable_t , double); + /* * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy. */ //solves the proportional fairness using a lagrange optimizition with dichotomy step void lagrange_solve(lmm_system_t sys); //computes the value of the dichotomy using a initial values, init, with a specific variable or constraint -double dichotomy(double init, double diff(double, void *), void *var_cnst, - double min_error); +static double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error); //computes the value of the differential of variable param_var applied to mu -double partial_diff_mu(double mu, void *param_var); +static double partial_diff_mu(double mu, void *param_var); //computes the value of the differential of constraint param_cnst applied to lambda -double partial_diff_lambda(double lambda, void *param_cnst); -//auxiliar function to compute the partial_diff -double diff_aux(lmm_variable_t var, double x); - +static double partial_diff_lambda(double lambda, void *param_cnst); -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 +50,6 @@ 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 +67,14 @@ 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)) { @@ -90,25 +84,81 @@ 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->mucnsts_number; i++) { + tmp += (var->cnsts[i].constraint)->lambda; + } + if (var->bound > 0) + tmp += var->mu; + DEBUG3("\t Working on var (%p). cost = %e; Df = %e", var, tmp, + var->df); + //uses the partial differential inverse function + return var->func_fpi(var, tmp); +} + +static double new_mu(lmm_variable_t var) +{ + double mu_i = 0.0; + double sigma_i = 0.0; + int j; + + for (j = 0; j < var->cnsts_number; j++) { + sigma_i += (var->cnsts[j].constraint)->lambda; + } + mu_i = var->func_fp(var,var->bound)-sigma_i; + if(mu_i<0.0) return 0.0; + return mu_i; +} + +static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list) +{ + lmm_constraint_t cnst = NULL; + lmm_variable_t var = NULL; + + double obj = 0.0; + + xbt_swag_foreach(var, var_list) { + double sigma_i=0.0; + int j; + + for (j = 0; j < var->cnsts_number; j++) + sigma_i += (var->cnsts[j].constraint)->lambda; + + if (var->bound > 0) + sigma_i += var->mu; + + DEBUG2("var %p : sigma_i = %1.20f",var,sigma_i); + + obj += var->func_f(var,var->func_fpi(var,sigma_i)) - + sigma_i*var->func_fpi(var,sigma_i); + + if (var->bound > 0) + obj += var->mu*var->bound; + } + + xbt_swag_foreach(cnst, cnst_list) + obj += cnst->lambda*cnst->bound; + + return obj; +} + void lagrange_solve(lmm_system_t sys) { /* * Lagrange Variables. */ int max_iterations = 100; - double epsilon_min_error = 1e-6; - double dichotomy_min_error = 1e-20; - double overall_error = 1; + double epsilon_min_error = MAXMIN_PRECISION; + double dichotomy_min_error = 1e-14; + double overall_modification = 1; /* * Variables to manipulate the data structure proposed to model the maxmin @@ -126,7 +176,7 @@ void lagrange_solve(lmm_system_t sys) int iteration = 0; double tmp = 0; int i; - + double obj,new_obj; DEBUG0("Iterative method configuration snapshot =====>"); DEBUG1("#### Maximum number of iterations : %d", max_iterations); @@ -138,6 +188,17 @@ void lagrange_solve(lmm_system_t sys) if (!(sys->modified)) return; + + /* + * Initialize lambda. + */ + cnst_list = &(sys->active_constraint_set); + xbt_swag_foreach(cnst, cnst_list) { + cnst->lambda = 1.0; + cnst->new_lambda = 2.0; + DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); + } + /* * Initialize the var list variable with only the active variables. * Associate an index in the swag variables. Initialize mu. @@ -148,9 +209,14 @@ void lagrange_solve(lmm_system_t sys) if ((var->bound < 0.0) || (var->weight <= 0.0)) { DEBUG1("#### NOTE var(%d) is a boundless (or inactive) variable", i); var->mu = -1.0; + if(var->weight>0.0) + var->value = new_value(var); + else + var->value = 0; } else { var->mu = 1.0; var->new_mu = 2.0; + var->value = new_value(var); } DEBUG3("#### var(%d) %p ->mu : %e", i, var, var->mu); DEBUG3("#### var(%d) %p ->weight: %e", i, var, var->weight); @@ -159,53 +225,58 @@ void lagrange_solve(lmm_system_t sys) } /* - * Initialize lambda. + * Compute dual objective. */ - cnst_list = &(sys->active_constraint_set); - xbt_swag_foreach(cnst, cnst_list) { - cnst->lambda = 1.0; - cnst->new_lambda = 2.0; - DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); - } + obj = dual_objective(var_list,cnst_list); /* * While doesn't reach a minimun error or a number maximum of iterations. */ - while (overall_error > epsilon_min_error && iteration < max_iterations) { - int dual_updated=0; + 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 + * Improve 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); - var->new_mu = - dichotomy(var->mu, partial_diff_mu, var, dichotomy_min_error); - 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)); + var->new_mu = new_mu(var); +/* 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; + + new_obj=dual_objective(var_list,cnst_list); + DEBUG3("Improvement for Objective (%g -> %g) : %g", obj, new_obj, + obj-new_obj); + xbt_assert1(obj-new_obj>=-epsilon_min_error,"Our gradient sucks! (%1.20f)",obj-new_obj); + obj = new_obj; } } /* - * Compute the value of lambda_i + * Improve the value of lambda_i */ - //forall lambda_i in lambda_1, lambda_2, ..., lambda_n xbt_swag_foreach(cnst, cnst_list) { DEBUG1("Working on cnst (%p)", cnst); cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error); - 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)); +/* 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; + + new_obj=dual_objective(var_list,cnst_list); + DEBUG3("Improvement for Objective (%g -> %g) : %g", obj, new_obj, + obj-new_obj); + xbt_assert1(obj-new_obj>=-epsilon_min_error,"Our gradient sucks! (%1.20f)",obj-new_obj); + obj = new_obj; } /* @@ -213,52 +284,38 @@ void lagrange_solve(lmm_system_t sys) * the values of \lambda and \mu. */ DEBUG0("-------------- Check convergence ----------"); - overall_error = 0; + overall_modification = 0; xbt_swag_foreach(var, var_list) { if (var->weight <= 0) var->value = 0.0; else { - //compute sigma_i + mu_i - tmp = 0; - for (i = 0; i < var->cnsts_number; i++) { - tmp += (var->cnsts[i].constraint)->lambda; - } - if (var->bound > 0) - tmp += var->mu; - DEBUG3("\t Working on var (%p). cost = %e; Df = %e", var, tmp, - var->df); - - //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))) { - overall_error = (fabs(var->value - tmp)); - } + tmp = new_value(var); + + overall_modification = MAX(overall_modification, fabs(var->value - tmp)); var->value = tmp; - DEBUG3("New value of var (%p) = %e, overall_error = %e", var, - var->value, overall_error); + DEBUG3("New value of var (%p) = %e, overall_modification = %e", var, + var->value, overall_modification); } } - if (!__check_kkt(cnst_list, var_list, 0)) - overall_error = 1.0; - DEBUG2("Iteration %d: Overall_error : %f", iteration, overall_error); - if(!dual_updated) { - WARN1("Could not improve the convergence at iteration %d. Drop it!",iteration); - break; - } + DEBUG0("-------------- Check feasability ----------"); + 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_feasible(cnst_list, var_list, 1); - __check_kkt(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); } @@ -281,8 +338,8 @@ void lagrange_solve(lmm_system_t sys) * * @return a double correponding to the result of the dichotomyal process */ -double dichotomy(double init, double diff(double, void *), void *var_cnst, - double min_error) +static double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error) { double min, max; double overall_error; @@ -312,7 +369,7 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, while (overall_error > min_error) { CDEBUG4(surf_lagrange_dichotomy, - "min, max = %1.20f, %1.20f || diffmin, diffmax %1.20f, %1.20f", min, max, + "[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) { @@ -324,7 +381,6 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = min; max_diff = min_diff; - } } else if (min_diff < 0 && max_diff < 0) { if (min == max) { @@ -341,7 +397,9 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, CDEBUG1(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",middle); if((min==middle) || (max==middle)) { - WARN0("Cannot improve the convergence!"); + CWARN4(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 ([%1.20f,%1.20f]).", + min, max-min, min_diff,max_diff); break; } middle_diff = diff(middle, var_cnst); @@ -349,20 +407,27 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, if (middle_diff < 0) { CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = middle; + overall_error = max_diff-middle_diff; min_diff = middle_diff; +/* SHOW_EXPR(overall_error); */ } else if (middle_diff > 0) { CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = middle; + overall_error = max_diff-middle_diff; max_diff = middle_diff; +/* SHOW_EXPR(overall_error); */ } else { overall_error = 0; +/* SHOW_EXPR(overall_error); */ } } else if (min_diff == 0) { max=min; overall_error = 0; +/* SHOW_EXPR(overall_error); */ } else if (max_diff == 0) { min=max; overall_error = 0; +/* SHOW_EXPR(overall_error); */ } else if (min_diff > 0 && max_diff < 0) { CWARN0(surf_lagrange_dichotomy, "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); @@ -380,63 +445,34 @@ double dichotomy(double init, double diff(double, void *), void *var_cnst, return ((min + max) / 2.0); } -/* - * - */ -double partial_diff_mu(double mu, void *param_var) -{ - double mu_partial = 0.0; - double sigma_mu = 0.0; - lmm_variable_t var = (lmm_variable_t) param_var; - int i; - XBT_IN; - //compute sigma_i - for (i = 0; i < var->cnsts_number; i++) - sigma_mu += (var->cnsts[i].constraint)->lambda; - - //compute sigma_i + mu_i - sigma_mu += mu; - - //use auxiliar function passing (sigma_i + mu_i) - mu_partial = diff_aux(var, sigma_mu); - - //add the RTT limit - mu_partial += var->bound; - - XBT_OUT; - return mu_partial; -} - -/* - * - */ -double partial_diff_lambda(double lambda, void *param_cnst) +static double partial_diff_lambda(double lambda, void *param_cnst) { - int i; + int j; xbt_swag_t elem_list = NULL; lmm_element_t elem = NULL; lmm_variable_t var = NULL; lmm_constraint_t cnst = (lmm_constraint_t) param_cnst; - double lambda_partial = 0.0; + double diff = 0.0; double sigma_i = 0.0; XBT_IN; elem_list = &(cnst->element_set); - CDEBUG1(surf_lagrange_dichotomy,"Computting diff of cnst (%p)", cnst); + CDEBUG1(surf_lagrange_dichotomy,"Computing diff of cnst (%p)", cnst); xbt_swag_foreach(elem, elem_list) { var = elem->variable; if (var->weight <= 0) continue; - //initilize de sumation variable + CDEBUG1(surf_lagrange_dichotomy,"Computing sigma_i for var (%p)", var); + // Initialize the summation variable sigma_i = 0.0; - //compute sigma_i of variable var - for (i = 0; i < var->cnsts_number; i++) { - sigma_i += (var->cnsts[i].constraint)->lambda; + // Compute sigma_i + for (j = 0; j < var->cnsts_number; j++) { + sigma_i += (var->cnsts[j].constraint)->lambda; } //add mu_i if this flow has a RTT constraint associated @@ -446,35 +482,32 @@ double partial_diff_lambda(double lambda, void *param_cnst) //replace value of cnst->lambda by the value of parameter lambda sigma_i = (sigma_i - cnst->lambda) + lambda; - //use the auxiliar function passing (\sigma_i + \mu_i) - lambda_partial += diff_aux(var, sigma_i); + diff += -var->func_fpi(var, sigma_i); } - lambda_partial += cnst->bound; - - -/* CDEBUG1(surf_lagrange_dichotomy, "returning = %1.20f", lambda_partial); */ + diff += cnst->bound; + CDEBUG3(surf_lagrange_dichotomy,"d D/d lambda for cnst (%p) at %1.20f = %1.20f", + cnst, lambda, diff); XBT_OUT; - return lambda_partial; + return diff; } - -double diff_aux(lmm_variable_t var, double x) +/** \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_f) (lmm_variable_t var, double x), + double (* func_fp) (lmm_variable_t var, double x), + double (* func_fpi) (lmm_variable_t var, double x)) { - double tmp_fpi, result; - - XBT_IN2("(var (%p), x (%1.20f))", var, x); - xbt_assert0(var->func_fp, - "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; + func_f_def = func_f; + func_fp_def = func_fp; + func_fpi_def = func_fpi; } @@ -485,85 +518,53 @@ double diff_aux(lmm_variable_t var, double x) */ /* - * For Vegas f: $\alpha_f d_f \log\left(x_f\right)$ + * For Vegas: $f(x) = \alpha D_f\ln(x)$ + * Therefore: $fp(x) = \frac{\alpha D_f}{x}$ + * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$ */ #define VEGAS_SCALING 1000.0 + double func_vegas_f(lmm_variable_t var, double x){ - return VEGAS_SCALING*var->df * log(x); + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + return VEGAS_SCALING*var->df*log(x); } -/* - * For Vegas fp: $\frac{\alpha D_f}{x}$ - */ double func_vegas_fp(lmm_variable_t var, double x){ - //avoid a disaster value - c'est du bricolage mais ca marche -/* if(x == 0) x = 10e-8; */ + xbt_assert0(x>0.0,"Don't call me with stupid values!"); return VEGAS_SCALING*var->df/x; } -/* - * For Vegas fpi: $\frac{\alpha D_f}{x}$ - */ double func_vegas_fpi(lmm_variable_t var, double x){ - //avoid a disaster value - c'est du bricolage mais ca marche -/* if(x == 0) x = 10e-8; */ - return VEGAS_SCALING*var->df/x; -} - -/* - * For Vegas fpip: $-\frac{\alpha D_f}{x^2}$ - */ -double func_vegas_fpip(lmm_variable_t var, double x){ - //avoid a disaster value - c'est du bricolage mais ca marche -/* if(x == 0) x = 10e-8; */ - return -( VEGAS_SCALING*var->df/(x*x) ) ; + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + return var->df/(x/VEGAS_SCALING); } - /* - * For Reno f: $\frac{\sqrt{\frac{3}{2}}}{D_f} \arctan\left(\sqrt{\frac{3}{2}}x_f D_f\right)$ + * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$ + * Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$ + * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ */ +#define RENO_SCALING 1.0 double func_reno_f(lmm_variable_t var, double x){ xbt_assert0(var->df>0.0,"Don't call me with stupid values!"); - // \sqrt{3/2} = 0.8164965808 - return (0.8164965808 / var->df) * atan( (0.8164965808 / var->df)*x ); + + return RENO_SCALING*sqrt(3.0/2.0)/var->df*atan(sqrt(3.0/2.0)*var->df*x); } -/* - * For Reno fp: $\frac{3}{3 {D_f}^2 x^2 + 2}$ - */ double func_reno_fp(lmm_variable_t var, double x){ - return 3 / (3*var->df*var->df*x*x + 2); + return RENO_SCALING*3.0/(3.0*var->df*var->df*x*x +2.0); } -/* - * For Reno fpi: $\sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ - */ 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); + res_fpi = 1.0/(var->df*var->df*(x/RENO_SCALING)) - 2.0/(3.0*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!"); +/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */ return sqrt(res_fpi); } -/* - * For Reno fpip: $-\frac{1}{2 {D_f}^2 x^2\sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}}$ - */ -double func_reno_fpip(lmm_variable_t var, double x){ - double res_fpip; - double critical_test; - 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_fpip = 1/(var->df*var->df*x) - 2/(3*var->df*var->df); - xbt_assert0(res_fpip>0.0,"Don't call me with stupid values!"); - critical_test = (2*var->df*var->df*x*x*sqrt(res_fpip)); - - return -(1.0/critical_test); -}