diff options
Diffstat (limited to 'newstructure/sci_gateway')
24 files changed, 0 insertions, 3348 deletions
diff --git a/newstructure/sci_gateway/builder_gateway.sce b/newstructure/sci_gateway/builder_gateway.sce deleted file mode 100644 index de09d9c..0000000 --- a/newstructure/sci_gateway/builder_gateway.sce +++ /dev/null @@ -1,18 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -sci_gateway_dir = get_absolute_file_path('builder_gateway.sce'); - -tbx_builder_gateway_lang('cpp', sci_gateway_dir); -tbx_build_gateway_loader(['cpp'], sci_gateway_dir); - -clear tbx_builder_gateway_lang tbx_build_gateway_loader; -clear sci_gateway_dir; diff --git a/newstructure/sci_gateway/cpp/QuadTMINLP.hpp b/newstructure/sci_gateway/cpp/QuadTMINLP.hpp deleted file mode 100644 index 84704be..0000000 --- a/newstructure/sci_gateway/cpp/QuadTMINLP.hpp +++ /dev/null @@ -1,134 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#ifndef QuadTMINLP_HPP -#define QuadTMINLP_HPP - -#include "BonTMINLP.hpp" -#include "IpTNLP.hpp" - -using namespace Ipopt; -using namespace Bonmin; - -class QuadTMINLP : public TMINLP -{ - private: - Index numVars_; // Number of variables. - - Index numCons_; // Number of constraints. - - Index intconSize_; // Number of integer constraints - - const Number *qMatrix_ = NULL; //qMatrix_ is a pointer to matrix of size numVars X numVars_ - // with coefficents of quadratic terms in objective function. - - const Number *lMatrix_ = NULL;//lMatrix_ is a pointer to matrix of size 1*numVars_ - // with coefficents of linear terms in objective function. - - const Number *intcon_ = NULL; // The matrix containing the integer constraints - - const Number *conMatrix_ = NULL;//conMatrix_ is a pointer to matrix of size numCons X numVars - // with coefficients of terms in a each objective in each row. - - const Number *conUB_= NULL; //conUB_ is a pointer to a matrix of size of 1*numCons_ - // with upper bounds of all constraints. - - const Number *conLB_ = NULL; //conLB_ is a pointer to a matrix of size of 1*numConsn_ - // with lower bounds of all constraints. - - const Number *varUB_= NULL; //varUB_ is a pointer to a matrix of size of 1*numVar_ - // with upper bounds of all variables. - - const Number *varLB_= NULL; //varLB_ is a pointer to a matrix of size of 1*numVar_ - // with lower bounds of all variables. - - const Number *varGuess_= NULL; //varGuess_ is a pointer to a matrix of size of 1*numVar_ - // with initial guess of all variables. - - Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVar_ - // with final value for the primal variables. - - Number *finalZl_= NULL; //finalZl_ is a pointer to a matrix of size of 1*numVar_ - // with final values for the lower bound multipliers - - Number *finalZu_= NULL; //finalZu_ is a pointer to a matrix of size of 1*numVar_ - // with final values for the upper bound multipliers - - Number *finalLambda_= NULL; //finalLambda_ is a pointer to a matrix of size of 1*numConstr_ - // with final values for the upper bound multipliers - - Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. - - int status_; //Solver return status - -public: - // Constructor - QuadTMINLP(Index nV, Index nC, Index intconSize,Number *qM, Number *lM, Number *intcon,Number *cM, Number *cLB, Number *cUB, Number *vLB, Number *vUB,Number *vG): - numVars_(nV),numCons_(nC),intconSize_(intconSize),qMatrix_(qM),lMatrix_(lM),intcon_(intcon),conMatrix_(cM),conLB_(cLB),conUB_(cUB),varLB_(vLB),varUB_(vUB),varGuess_(vG), finalObjVal_(0){ } - - // virtual destructor. - virtual ~QuadTMINLP(){} - - /* Copy constructor.*/ - QuadTMINLP(const QuadTMINLP &other){} - - // Go to http://coin-or.org/Bonmin for the details of the below methods - - virtual bool get_variables_types(Index n, VariableType* var_types); - - virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types); - - virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types); - - - virtual bool get_nlp_info(Index& n, Index&m, Index& nnz_jac_g, - Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style); - - virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, - Index m, Number* g_l, Number* g_u); - - virtual bool get_starting_point(Index n, bool init_x, Number* x, - bool init_z, Number* z_L, Number* z_U, - Index m, bool init_lambda, - Number* lambda); - - virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); - - virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); - - virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); - - virtual bool eval_jac_g(Index n, const Number* x, bool new_x, - Index m, Index nele_jac, Index* iRow, Index *jCol, - Number* values); - - virtual bool eval_h(Index n, const Number* x, bool new_x, - Number obj_factor, Index m, const Number* lambda, - bool new_lambda, Index nele_hess, Index* iRow, - Index* jCol, Number* values); - - virtual void QuadTMINLP::finalize_solution(TMINLP::SolverReturn status, - Index n, const Number* x,Number obj_value); - - virtual const SosInfo * sosConstraints() const{return NULL;} - virtual const BranchingInfo* branchingInfo() const{return NULL;} - - // Bonmin methods end here - - virtual const double * getX(); //Returns a pointer to a matrix of size of 1*numVar - // with final value for the primal variables. - - virtual double getObjVal(); //Returns the output of the final value of the objective. - - virtual int returnStatus(); //Returns the status count -}; - -#endif diff --git a/newstructure/sci_gateway/cpp/bonmin.opt b/newstructure/sci_gateway/cpp/bonmin.opt deleted file mode 100644 index 72fc254..0000000 --- a/newstructure/sci_gateway/cpp/bonmin.opt +++ /dev/null @@ -1,156 +0,0 @@ - -# registering category: Algorithm choice - -bonmin.algorithm B-BB #Choice of the algorithm. - -# registering category: Branch-and-bound options - -bonmin.allowable_fraction_gap 0 #Specify the value of relative gap under which the algorithm stops. -bonmin.allowable_gap 0 #Specify the value of absolute gap under which the algorithm stops. -bonmin.cutoff 1e+100 #Specify cutoff value. -bonmin.cutoff_decr 1e-05 #Specify cutoff decrement. -bonmin.enable_dynamic_nlp no #Enable dynamic linear and quadratic rows addition in nlp -bonmin.integer_tolerance 1e-06 #Set integer tolerance. -bonmin.iteration_limit 2147483647 #Set the cumulative maximum number of iteration in the algorithm used to process nodes continuous relaxations in the branch-and-bound. -bonmin.nlp_failure_behavior stop #Set the behavior when an NLP or a series of NLP are unsolved by Ipopt (we call unsolved an NLP for which Ipopt is not able to guarantee optimality within the specified tolerances). -bonmin.node_comparison best-bound #Choose the node selection strategy. -bonmin.node_limit 2147483647 #Set the maximum number of nodes explored in the branch-and-bound search. -bonmin.num_cut_passes 1 #Set the maximum number of cut passes at regular nodes of the branch-and-cut. -bonmin.num_cut_passes_at_root 20 #Set the maximum number of cut passes at regular nodes of the branch-and-cut. -bonmin.number_before_trust 8 #Set the number of branches on a variable before its pseudo costs are to be believed in dynamic strong branching. -bonmin.number_strong_branch 20 #Choose the maximum number of variables considered for strong branching. -bonmin.random_generator_seed 0 #Set seed for random number generator (a value of -1 sets seeds to time since Epoch). -bonmin.read_solution_file no #Read a file with the optimal solution to test if algorithms cuts it. -bonmin.solution_limit 2147483647 #Abort after that much integer feasible solution have been found by algorithm -bonmin.sos_constraints enable #Whether or not to activate SOS constraints. -bonmin.time_limit 1e+10 #Set the global maximum computation time (in secs) for the algorithm. -bonmin.tree_search_strategy probed-dive #Pick a strategy for traversing the tree -bonmin.variable_selection strong-branching #Chooses variable selection strategy - -# registering category: ECP cuts generation - -bonmin.ecp_abs_tol 1e-06 #Set the absolute termination tolerance for ECP rounds. -bonmin.ecp_max_rounds 5 #Set the maximal number of rounds of ECP cuts. -bonmin.ecp_probability_factor 10 #Factor appearing in formula for skipping ECP cuts. -bonmin.ecp_rel_tol 0 #Set the relative termination tolerance for ECP rounds. -bonmin.filmint_ecp_cuts 0 #Specify the frequency (in terms of nodes) at which some a la filmint ecp cuts are generated. - -# registering category: Feasibility checker using OA cuts - -bonmin.feas_check_cut_types outer-approx #Choose the type of cuts generated when an integer feasible solution is found -bonmin.feas_check_discard_policy detect-cycles #How cuts from feasibility checker are discarded -bonmin.generate_benders_after_so_many_oa 5000 #Specify that after so many oa cuts have been generated Benders cuts should be generated instead. - -# registering category: MILP Solver - -bonmin.cpx_parallel_strategy 0 #Strategy of parallel search mode in CPLEX. -bonmin.milp_solver Cbc_D #Choose the subsolver to solve MILP sub-problems in OA decompositions. -bonmin.milp_strategy solve_to_optimality #Choose a strategy for MILPs. -bonmin.number_cpx_threads 0 #Set number of threads to use with cplex. - -# registering category: MILP cutting planes in hybrid algorithm - -bonmin.2mir_cuts 0 #Frequency (in terms of nodes) for generating 2-MIR cuts in branch-and-cut -bonmin.Gomory_cuts -5 #Frequency (in terms of nodes) for generating Gomory cuts in branch-and-cut. -bonmin.clique_cuts -5 #Frequency (in terms of nodes) for generating clique cuts in branch-and-cut -bonmin.cover_cuts 0 #Frequency (in terms of nodes) for generating cover cuts in branch-and-cut -bonmin.flow_cover_cuts -5 #Frequency (in terms of nodes) for generating flow cover cuts in branch-and-cut -bonmin.lift_and_project_cuts 0 #Frequency (in terms of nodes) for generating lift-and-project cuts in branch-and-cut -bonmin.mir_cuts -5 #Frequency (in terms of nodes) for generating MIR cuts in branch-and-cut -bonmin.reduce_and_split_cuts 0 #Frequency (in terms of nodes) for generating reduce-and-split cuts in branch-and-cut - -# registering category: NLP interface - -bonmin.nlp_solver Ipopt #Choice of the solver for local optima of continuous NLP's -bonmin.warm_start none #Select the warm start method - -# registering category: NLP solution robustness - -bonmin.max_consecutive_failures 10 #(temporarily removed) Number $n$ of consecutive unsolved problems before aborting a branch of the tree. -bonmin.max_random_point_radius 100000 #Set max value r for coordinate of a random point. -bonmin.num_iterations_suspect -1 #Number of iterations over which a node is considered "suspect" (for debugging purposes only, see detailed documentation). -bonmin.num_retry_unsolved_random_point 0 #Number $k$ of times that the algorithm will try to resolve an unsolved NLP with a random starting point (we call unsolved an NLP for which Ipopt is not able to guarantee optimality within the specified tolerances). -bonmin.random_point_perturbation_interval 1 #Amount by which starting point is perturbed when choosing to pick random point by perturbing starting point -bonmin.random_point_type Jon #method to choose a random starting point -bonmin.resolve_on_small_infeasibility 0 #If a locally infeasible problem is infeasible by less than this, resolve it with initial starting point. - -# registering category: NLP solves in hybrid algorithm (B-Hyb) - -bonmin.nlp_solve_frequency 10 #Specify the frequency (in terms of nodes) at which NLP relaxations are solved in B-Hyb. -bonmin.nlp_solve_max_depth 10 #Set maximum depth in the tree at which NLP relaxations are solved in B-Hyb. -bonmin.nlp_solves_per_depth 1e+100 #Set average number of nodes in the tree at which NLP relaxations are solved in B-Hyb for each depth. - -# registering category: Nonconvex problems - -bonmin.coeff_var_threshold 0.1 #Coefficient of variation threshold (for dynamic definition of cutoff_decr). -bonmin.dynamic_def_cutoff_decr no #Do you want to define the parameter cutoff_decr dynamically? -bonmin.first_perc_for_cutoff_decr -0.02 #The percentage used when, the coeff of variance is smaller than the threshold, to compute the cutoff_decr dynamically. -bonmin.max_consecutive_infeasible 0 #Number of consecutive infeasible subproblems before aborting a branch. -bonmin.num_resolve_at_infeasibles 0 #Number $k$ of tries to resolve an infeasible node (other than the root) of the tree with different starting point. -bonmin.num_resolve_at_node 0 #Number $k$ of tries to resolve a node (other than the root) of the tree with different starting point. -bonmin.num_resolve_at_root 0 #Number $k$ of tries to resolve the root node with different starting points. -bonmin.second_perc_for_cutoff_decr -0.05 #The percentage used when, the coeff of variance is greater than the threshold, to compute the cutoff_decr dynamically. - -# registering category: Outer Approximation Decomposition (B-OA) - -bonmin.oa_decomposition no #If yes do initial OA decomposition - -# registering category: Outer Approximation cuts generation - -bonmin.add_only_violated_oa no #Do we add all OA cuts or only the ones violated by current point? -bonmin.oa_cuts_scope global #Specify if OA cuts added are to be set globally or locally valid -bonmin.oa_rhs_relax 1e-08 #Value by which to relax OA cut -bonmin.tiny_element 1e-08 #Value for tiny element in OA cut -bonmin.very_tiny_element 1e-17 #Value for very tiny element in OA cut - -# registering category: Output - -bonmin.bb_log_interval 100 #Interval at which node level output is printed. -bonmin.bb_log_level 1 #specify main branch-and-bound log level. -bonmin.file_print_level 5 #Verbosity level for output file. -bonmin.file_solution no #Write a file bonmin.sol with the solution -bonmin.fp_log_frequency 100 #display an update on lower and upper bounds in FP every n seconds -bonmin.fp_log_level 1 #specify FP iterations log level. -bonmin.inf_pr_output original #Determines what value is printed in the "inf_pr" output column. -bonmin.lp_log_level 0 #specify LP log level. -bonmin.milp_log_level 0 #specify MILP solver log level. -bonmin.nlp_log_at_root 0 # Specify a different log level for root relaxation. -bonmin.nlp_log_level 1 #specify NLP solver interface log level (independent from ipopt print_level). -bonmin.oa_cuts_log_level 0 #level of log when generating OA cuts. -bonmin.oa_log_frequency 100 #display an update on lower and upper bounds in OA every n seconds -bonmin.oa_log_level 1 #specify OA iterations log level. -bonmin.option_file_name #File name of options file (to overwrite default). -bonmin.output_file #File name of desired output file (leave unset for no file output). -bonmin.print_frequency_iter 1 #Determines at which iteration frequency the summarizing iteration output line should be printed. -bonmin.print_frequency_time 0 #Determines at which time frequency the summarizing iteration output line should be printed. -bonmin.print_info_string no #Enables printing of additional info string at end of iteration output. -bonmin.print_level 5 #Output verbosity level. -bonmin.print_options_documentation no #Switch to print all algorithmic options. -bonmin.print_timing_statistics no #Switch to print timing statistics. -bonmin.print_user_options no #Print all options set by the user. -bonmin.replace_bounds no #Indicates if all variable bounds should be replaced by inequality constraints -bonmin.skip_finalize_solution_call no #Indicates if call to NLP::FinalizeSolution after optimization should be suppressed - -# registering category: Primal Heuristics - -bonmin.feasibility_pump_objective_norm 1 #Norm of feasibility pump objective function -bonmin.fp_pass_infeasible no #Say whether feasibility pump should claim to converge or not -bonmin.heuristic_RINS no #if yes runs the RINS heuristic -bonmin.heuristic_dive_MIP_fractional no #if yes runs the Dive MIP Fractional heuristic -bonmin.heuristic_dive_MIP_vectorLength no #if yes runs the Dive MIP VectorLength heuristic -bonmin.heuristic_dive_fractional no #if yes runs the Dive Fractional heuristic -bonmin.heuristic_dive_vectorLength no #if yes runs the Dive VectorLength heuristic -bonmin.heuristic_feasibility_pump no #whether the heuristic feasibility pump should be used -bonmin.pump_for_minlp no #whether to run the feasibility pump heuristic for MINLP - -# registering category: Strong branching setup - -bonmin.candidate_sort_criterion best-ps-cost #Choice of the criterion to choose candidates in strong-branching -bonmin.maxmin_crit_have_sol 0.1 #Weight towards minimum in of lower and upper branching estimates when a solution has been found. -bonmin.maxmin_crit_no_sol 0.7 #Weight towards minimum in of lower and upper branching estimates when no solution has been found yet. -bonmin.min_number_strong_branch 0 #Sets minimum number of variables for strong branching (overriding trust) -bonmin.number_before_trust_list 0 #Set the number of branches on a variable before its pseudo costs are to be believed during setup of strong branching candidate list. -bonmin.number_look_ahead 0 #Sets limit of look-ahead strong-branching trials -bonmin.number_strong_branch_root 2147483647 #Maximum number of variables considered for strong branching in root node. -bonmin.setup_pseudo_frac 0.5 #Proportion of strong branching list that has to be taken from most-integer-infeasible list. -bonmin.trust_strong_branching_for_pseudo_cost yes #Whether or not to trust strong branching results for updating pseudo costs. diff --git a/newstructure/sci_gateway/cpp/builder_gateway_cpp.sce b/newstructure/sci_gateway/cpp/builder_gateway_cpp.sce deleted file mode 100755 index 1359db8..0000000 --- a/newstructure/sci_gateway/cpp/builder_gateway_cpp.sce +++ /dev/null @@ -1,68 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -mode(-1) -lines(0) - -toolbox_title = "FOSSEE_Optimization_Toolbox"; - -Build_64Bits = %t; - -path_builder = get_absolute_file_path('builder_gateway_cpp.sce'); - -//Name of All the Functions -Function_Names = [ - 'inter_fminunc', 'cpp_intfminunc'; - 'inter_fminbnd', 'cpp_intfminbnd'; - 'inter_fmincon', 'cpp_intfmincon'; - 'sci_intqpipopt', 'cpp_intqpipopt'; - 'sci_matrix_intlinprog', 'matrix_cppintlinprog'; - 'sci_mps_intlinprog','mps_cppintlinprog'; - ]; - -//Name of all the files to be compiled -Files = [ - 'sci_iofunc.cpp', - 'sci_minuncTMINLP.cpp', - 'cpp_intfminunc.cpp', - 'sci_minbndTMINLP.cpp', - 'cpp_intfminbnd.cpp', - 'sci_minconTMINLP.cpp', - 'cpp_intfmincon.cpp', - 'cbcintlinprog_matrixcpp.cpp', - 'sci_QuadTMINLP.cpp', - 'cpp_intqpipopt.cpp', - 'cbcintlinprog_mpscpp.cpp' - ] - -[a, opt] = getversion(); -Version = opt(2); - -if getos()=="Windows" then - third_dir = path_builder+filesep()+'..'+filesep()+'..'+filesep()+'thirdparty'; - lib_base_dir = third_dir + filesep() + 'windows' + filesep() + 'lib' + filesep() + Version + filesep(); - inc_base_dir = third_dir + filesep() + 'windows' + filesep() + 'include' + filesep() + 'coin'; -// C_Flags=['-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -I '+path_builder+' '+ '-I '+inc_base_dir+' '] -// Linker_Flag = [lib_base_dir+"libClp.lib "+lib_base_dir+"libCgl.lib "+lib_base_dir+"libOsi.lib "+lib_base_dir+"libOsiClp.lib "+lib_base_dir+"libCoinUtils.lib "+lib_base_dir+"libSymphony.lib "+lib_base_dir+"IpOptFSS.lib "+lib_base_dir+"IpOpt-vc10.lib "] - -else - third_dir = path_builder+filesep()+'..'+filesep()+'..'+filesep()+'thirdparty'; - lib_base_dir = third_dir + filesep() + 'linux' + filesep() + 'lib' + filesep() + Version + filesep(); - inc_base_dir = third_dir + filesep() + 'linux' + filesep() + 'include' + filesep() + 'coin'; - - C_Flags=["-D__USE_DEPRECATED_STACK_FUNCTIONS__ -w -fpermissive -I"+path_builder+" -I"+inc_base_dir+" -Wl,-rpath="+lib_base_dir+" "] - - Linker_Flag = ["-L"+lib_base_dir+"libCoinUtils.so "+"-L"+lib_base_dir+"libClp.so "+"-L"+lib_base_dir+"libClpSolver.so "+"-L"+lib_base_dir+"libOsi.so "+"-L"+lib_base_dir+"libOsiClp.so "+"-L"+lib_base_dir+"libCgl.so "+"-L"+lib_base_dir+"libCbc.so "+"-L"+lib_base_dir+"libCbcSolver.so "+"-L"+lib_base_dir+"libOsiCbc.so "+"-L"+lib_base_dir+"libipopt.so "+"-L"+lib_base_dir+"libbonmin.so " ] -end - -tbx_build_gateway(toolbox_title,Function_Names,Files,get_absolute_file_path("builder_gateway_cpp.sce"), [], Linker_Flag, C_Flags); - -clear toolbox_title Function_Names Files Linker_Flag C_Flags; diff --git a/newstructure/sci_gateway/cpp/cbcintlinprog_matrixcpp.cpp b/newstructure/sci_gateway/cpp/cbcintlinprog_matrixcpp.cpp deleted file mode 100644 index d4e6f41..0000000 --- a/newstructure/sci_gateway/cpp/cbcintlinprog_matrixcpp.cpp +++ /dev/null @@ -1,226 +0,0 @@ -// MILP with CBC library, Matrix -// Code Authors: Akshay Miterani and Pranav Deshpande - -#include <sci_iofunc.hpp> - -// For Branch and bound -#include "OsiSolverInterface.hpp" -#include "CbcModel.hpp" -#include "CbcCutGenerator.hpp" -#include "CbcHeuristicLocal.hpp" -#include "OsiClpSolverInterface.hpp" -extern "C"{ -#include <api_scilab.h> -#include "sciprint.h" - -int matrix_cppintlinprog(){ - - //Objective function - double* obj; - //Constraint matrix coefficients - double* conMatrix; - //intcon Matrix - double* intcon; - //Constraints upper bound - double* conlb; - //Constraints lower bound - double* conub; - //Lower bounds for variables - double* lb; - //Upper bounds for variables - double* ub; - //options for maximum iterations and writing mps - double* options; - //Flag for Mps - double flagMps; - //mps file path - char * mpsFile; - //Error structure in Scilab - SciErr sciErr; - //Number of rows and columns in objective function - int nVars=0, nCons=0,temp1=0,temp2=0; - int numintcons=0; - double valobjsense; - - CheckInputArgument(pvApiCtx , 11 , 11); //Checking the input arguments - CheckOutputArgument(pvApiCtx , 8, 8); //Checking the output arguments - - ////////// Manage the input argument ////////// - - //Number of Variables - if(getIntFromScilab(1,&nVars)) - { - return 1; - } - - //Number of Constraints - if (getIntFromScilab(2,&nCons)) - { - return 1; - } - - //Objective function from Scilab - temp1 = nVars; - temp2 = nCons; - if (getFixedSizeDoubleMatrixFromScilab(3,1,temp1,&obj)) - { - return 1; - } - - //intcon matrix - if (getDoubleMatrixFromScilab(4,&numintcons,&temp2,&intcon)) - { - return 1; - } - - if (nCons!=0) - { - //conMatrix matrix from scilab - temp1 = nCons; - temp2 = nVars; - - if (getFixedSizeDoubleMatrixFromScilab(5,temp1,temp2,&conMatrix)) - { - return 1; - } - - //conLB matrix from scilab - temp1 = nCons; - temp2 = 1; - if (getFixedSizeDoubleMatrixFromScilab(6,temp1,temp2,&conlb)) - { - return 1; - } - - //conUB matrix from scilab - if (getFixedSizeDoubleMatrixFromScilab(7,temp1,temp2,&conub)) - { - return 1; - } - - } - - //lb matrix from scilab - temp1 = 1; - temp2 = nVars; - if (getFixedSizeDoubleMatrixFromScilab(8,temp1,temp2,&lb)) - { - return 1; - } - - - //ub matrix from scilab - if (getFixedSizeDoubleMatrixFromScilab(9,temp1,temp2,&ub)) - { - return 1; - } - - //Object Sense - if(getDoubleFromScilab(10,&valobjsense)) - { - return 1; - } - - //get options from scilab - if(getFixedSizeDoubleMatrixFromScilab(11 , 1 , 4 , &options)) - { - return 1; - } - - //------------Temporary Version to make coin packed matrix------ - OsiClpSolverInterface solver1; - - CoinPackedMatrix *matrix = new CoinPackedMatrix(false , 0 , 0); - matrix->setDimensions(0 , nVars); - for(int i=0 ; i<nCons ; i++) - { - CoinPackedVector row; - for(int j=0 ; j<nVars ; j++) - { - row.insert(j, conMatrix[i+j*nCons]); - } - matrix->appendRow(row); - } - - - solver1.loadProblem(*matrix, lb, ub, obj, conlb, conub); - - for(int i=0;i<numintcons;i++) - solver1.setInteger(intcon[i]-1); - - solver1.setObjSense(valobjsense); - - //------------------------------------------------------------- - - CbcModel model(solver1); - - model.solver()->setHintParam(OsiDoReducePrint, true, OsiHintTry); - - if((int)options[0]!=0) - model.setIntegerTolerance(options[0]); - if((int)options[1]!=0) - model.setMaximumNodes((int)options[1]); - if((int)options[2]!=0) - model.setMaximumSeconds(options[2]); - if((int)options[3]!=0) - model.setAllowableGap(options[3]); - - model.branchAndBound(); - - const double *val = model.getColSolution(); - - //Output the solution to Scilab - - //get solution for x - double* xValue = model.getColSolution(); - - //get objective value - double objValue = model.getObjValue(); - - //Output status - double status_=-1; - if(model.isProvenOptimal()){ - status_=0; - } - else if(model.isProvenInfeasible()){ - status_=1; - } - else if(model.isSolutionLimitReached()){ - status_=2; - } - else if(model. isNodeLimitReached()){ - status_=3; - } - else if(model.isAbandoned()){ - status_=4; - } - else if(model.isSecondsLimitReached()){ - status_=5; - } - else if(model.isContinuousUnbounded()){ - status_=6; - } - else if(model.isProvenDualInfeasible()){ - status_=7; - } - - double nodeCount=model.getNodeCount(); - double nfps=model.numberIntegers(); - double U=model.getObjValue(); - double L=model.getBestPossibleObjValue(); - double iterCount=model.getIterationCount(); - - returnDoubleMatrixToScilab(1 , nVars, 1 , xValue); - returnDoubleMatrixToScilab(2 , 1 , 1 , &objValue); - returnDoubleMatrixToScilab(3 , 1 , 1 , &status_); - returnDoubleMatrixToScilab(4 , 1 , 1 , &nodeCount); - returnDoubleMatrixToScilab(5 , 1 , 1 , &nfps); - returnDoubleMatrixToScilab(6 , 1 , 1 , &L); - returnDoubleMatrixToScilab(7 , 1 , 1 , &U); - returnDoubleMatrixToScilab(8 , 1 , 1 , &iterCount); - - //------------------------------------------------------------- - - return 0; -} -} diff --git a/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp b/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp deleted file mode 100644 index 8292ab1..0000000 --- a/newstructure/sci_gateway/cpp/cbcintlinprog_mpscpp.cpp +++ /dev/null @@ -1,115 +0,0 @@ -// MILP with CBC library, mps -// Finds the solution by using CBC Library -// Code Authors: Akshay Miterani and Pranav Deshpande - -#include <sci_iofunc.hpp> - -// For Branch and bound -#include "OsiSolverInterface.hpp" -#include "CbcModel.hpp"= -#include "CbcCutGenerator.hpp" -#include "CbcHeuristicLocal.hpp" -#include "OsiClpSolverInterface.hpp" -extern "C" { -#include <api_scilab.h> - -int mps_cppintlinprog() -{ - OsiClpSolverInterface solver; - - // Path to the MPS file - char *mpsFilePath; - - // Options to set maximum iterations - double *options; - - // Input - 1 or 2 arguments allowed. - CheckInputArgument(pvApiCtx, 2, 2); - - // Get the MPS File Path from Scilab - getStringFromScilab(1, &mpsFilePath); - - // Receive the options for setting the maximum number of iterations etc. - if( getFixedSizeDoubleMatrixFromScilab(2, 1, 4, &options)) - { - return 1; - } - - // Read the MPS file - solver.readMps(mpsFilePath); - - // Cbc Library used from here - CbcModel model(solver); - - model.solver()->setHintParam(OsiDoReducePrint, true, OsiHintTry); - - if((int)options[0]!=0) - model.setIntegerTolerance(options[0]); - if((int)options[1]!=0) - model.setMaximumNodes((int)options[1]); - if((int)options[2]!=0) - model.setMaximumSeconds(options[2]); - if((int)options[3]!=0) - model.setAllowableGap(options[3]); - - model.branchAndBound(); - - int nVars = model.getNumCols(); - int nCons = model.getNumRows(); - - const double *val = model.getColSolution(); - - //Output the solution to Scilab - - //get solution for x - double* xValue = model.getColSolution(); - - //get objective value - double objValue = model.getObjValue(); - - //Output status - double status_=-1; - if(model.isProvenOptimal()){ - status_=0; - } - else if(model.isProvenInfeasible()){ - status_=1; - } - else if(model.isSolutionLimitReached()){ - status_=2; - } - else if(model. isNodeLimitReached()){ - status_=3; - } - else if(model.isAbandoned()){ - status_=4; - } - else if(model.isSecondsLimitReached()){ - status_=5; - } - else if(model.isContinuousUnbounded()){ - status_=6; - } - else if(model.isProvenDualInfeasible()){ - status_=7; - } - - double nodeCount = model.getNodeCount(); - double nfps = model.numberIntegers(); - double U = model.getObjValue(); - double L = model.getBestPossibleObjValue(); - double iterCount = model.getIterationCount(); - - returnDoubleMatrixToScilab(1 , nVars, 1 , xValue); - returnDoubleMatrixToScilab(2 , 1 , 1 , &objValue); - returnDoubleMatrixToScilab(3 , 1 , 1 , &status_); - returnDoubleMatrixToScilab(4 , 1 , 1 , &nodeCount); - returnDoubleMatrixToScilab(5 , 1 , 1 , &nfps); - returnDoubleMatrixToScilab(6 , 1 , 1 , &L); - returnDoubleMatrixToScilab(7 , 1 , 1 , &U); - returnDoubleMatrixToScilab(8 , 1 , 1 , &iterCount); - - return 0; -} - -} diff --git a/newstructure/sci_gateway/cpp/cleaner.sce b/newstructure/sci_gateway/cpp/cleaner.sce deleted file mode 100755 index 333775c..0000000 --- a/newstructure/sci_gateway/cpp/cleaner.sce +++ /dev/null @@ -1,22 +0,0 @@ -// This file is released under the 3-clause BSD license. See COPYING-BSD. -// Generated by builder.sce : Please, do not edit this file -// cleaner.sce -// ------------------------------------------------------ -curdir = pwd(); -cleaner_path = get_file_path('cleaner.sce'); -chdir(cleaner_path); -// ------------------------------------------------------ -if fileinfo('loader.sce') <> [] then - mdelete('loader.sce'); -end -// ------------------------------------------------------ -if fileinfo('libFOSSEE_Optimization_Toolbox.so') <> [] then - mdelete('libFOSSEE_Optimization_Toolbox.so'); -end -// ------------------------------------------------------ -if fileinfo('libFOSSEE_Optimization_Toolbox.c') <> [] then - mdelete('libFOSSEE_Optimization_Toolbox.c'); -end -// ------------------------------------------------------ -chdir(curdir); -// ------------------------------------------------------ diff --git a/newstructure/sci_gateway/cpp/cpp_intfminbnd.cpp b/newstructure/sci_gateway/cpp/cpp_intfminbnd.cpp deleted file mode 100644 index 4914111..0000000 --- a/newstructure/sci_gateway/cpp/cpp_intfminbnd.cpp +++ /dev/null @@ -1,172 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#include "CoinPragma.hpp" -#include "CoinTime.hpp" -#include "CoinError.hpp" - -#include "BonOsiTMINLPInterface.hpp" -#include "BonIpoptSolver.hpp" -#include "minbndTMINLP.hpp" -#include "BonCbc.hpp" -#include "BonBonminSetup.hpp" - -#include "BonOACutGenerator2.hpp" -#include "BonEcpCuts.hpp" -#include "BonOaNlpOptim.hpp" - -#include "sci_iofunc.hpp" -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - -int cpp_intfminbnd(char *fname) -{ - using namespace Ipopt; - using namespace Bonmin; - - CheckInputArgument(pvApiCtx, 8, 8); - CheckOutputArgument(pvApiCtx, 3, 3); - - // Input arguments - Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL, *lb = NULL, *ub = NULL; - static unsigned int nVars = 0; - unsigned int temp1 = 0,temp2 = 0, iret = 0; - int x0_rows, x0_cols,intconSize; - Number *intcon = NULL,*options=NULL, *ifval=NULL; - - // Output arguments - Number *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0; - Number dual_inf, constr_viol, complementarity, kkt_error; - int rstatus = 0; - - if(getDoubleMatrixFromScilab(4, &x0_rows, &x0_cols, &lb)) - { - return 1; - } - - if(getDoubleMatrixFromScilab(5, &x0_rows, &x0_cols, &ub)) - { - return 1; - } - - // Getting intcon - if (getDoubleMatrixFromScilab(6,&intconSize,&temp2,&intcon)) - { - return 1; - } - - //Initialization of parameters - nVars=x0_rows; - temp1 = 1; - temp2 = 1; - - //Getting parameters - if (getFixedSizeDoubleMatrixInList(7,2,temp1,temp2,&integertolerance)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(7,4,temp1,temp2,&maxnodes)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(7,6,temp1,temp2,&cputime)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(7,8,temp1,temp2,&allowablegap)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(7,10,temp1,temp2,&max_iter)) - { - return 1; - } - - SmartPtr<minbndTMINLP> tminlp = new minbndTMINLP(nVars,lb,ub,intconSize,intcon); - - BonminSetup bonmin; - bonmin.initializeOptionsAndJournalist(); - - bonmin.options()->SetStringValue("mu_oracle","loqo"); - bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance); - bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes); - bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime); - bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap); - bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter); - - //Now initialize from tminlp - bonmin.initialize(GetRawPtr(tminlp)); - - //Set up done, now let's branch and bound - try { - Bab bb; - bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc - } - catch(TNLPSolver::UnsolvedError *E) { - Scierror(999, "\nIpopt has failed to solve the problem!\n"); - } - catch(OsiTMINLPInterface::SimpleError &E) { - Scierror(999, "\nFailed to solve a problem!\n"); - } - catch(CoinError &E) { - Scierror(999, "\nFailed to solve a problem!\n"); - } - rstatus=tminlp->returnStatus(); - - if(rstatus==0 ||rstatus== 3) - { - fX = tminlp->getX(); - ObjVal = tminlp->getObjVal(); - if (returnDoubleMatrixToScilab(1, nVars, 1, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - else - { - if (returnDoubleMatrixToScilab(1, 0, 0, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - - return 0; - } -} - diff --git a/newstructure/sci_gateway/cpp/cpp_intfmincon.cpp b/newstructure/sci_gateway/cpp/cpp_intfmincon.cpp deleted file mode 100644 index d921128..0000000 --- a/newstructure/sci_gateway/cpp/cpp_intfmincon.cpp +++ /dev/null @@ -1,189 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#include "CoinPragma.hpp" -#include "CoinTime.hpp" -#include "CoinError.hpp" - -#include "BonOsiTMINLPInterface.hpp" -#include "BonIpoptSolver.hpp" -#include "minconTMINLP.hpp" -#include "BonCbc.hpp" -#include "BonBonminSetup.hpp" - -#include "BonOACutGenerator2.hpp" -#include "BonEcpCuts.hpp" -#include "BonOaNlpOptim.hpp" - -#include "sci_iofunc.hpp" -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - -int cpp_intfmincon(char *fname) -{ - using namespace Ipopt; - using namespace Bonmin; - - CheckInputArgument(pvApiCtx, 13, 13); - CheckOutputArgument(pvApiCtx, 3, 3); - - // Input arguments - Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL; - Number *x0 = NULL, *lb = NULL, *ub = NULL,*conLb = NULL, *conUb = NULL,*LC = NULL; - static unsigned int nVars = 0,nCons = 0; - unsigned int temp1 = 0,temp2 = 0, iret = 0; - int x0_rows, x0_cols,intconSize; - Number *intcon = NULL,*options=NULL, *ifval=NULL; - - // Output arguments - Number *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0; - Number dual_inf, constr_viol, complementarity, kkt_error; - int rstatus = 0; - - if(getDoubleMatrixFromScilab(6, &nVars, &x0_cols, &x0)) - { - return 1; - } - - if(getDoubleMatrixFromScilab(7, &x0_rows, &x0_cols, &lb)) - { - return 1; - } - - if(getDoubleMatrixFromScilab(8, &x0_rows, &x0_cols, &ub)) - { - return 1; - } - - if(getDoubleMatrixFromScilab(9, &nCons, &x0_cols, &conLb)) - { - return 1; - } - - if(getDoubleMatrixFromScilab(10, &x0_rows, &x0_cols, &conUb)) - { - return 1; - } - - // Getting intcon - if (getDoubleMatrixFromScilab(11,&intconSize,&temp2,&intcon)) - { - return 1; - } - - if (getDoubleMatrixFromScilab(13,&temp1,&temp2,&LC)) - { - return 1; - } - - //Initialization of parameters - temp1 = 1; - temp2 = 1; - - //Getting parameters - if (getFixedSizeDoubleMatrixInList(12,2,temp1,temp2,&integertolerance)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(12,4,temp1,temp2,&maxnodes)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(12,6,temp1,temp2,&cputime)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(12,8,temp1,temp2,&allowablegap)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(12,10,temp1,temp2,&max_iter)) - { - return 1; - } - - SmartPtr<minconTMINLP> tminlp = new minconTMINLP(nVars,x0,lb,ub,(unsigned int)LC,nCons,conLb,conUb,intconSize,intcon); - - BonminSetup bonmin; - bonmin.initializeOptionsAndJournalist(); - bonmin.options()->SetStringValue("mu_oracle","loqo"); - bonmin.options()->SetIntegerValue("bonmin.print_level",5); - bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance); - bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes); - bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime); - bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap); - bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter); - - //Now initialize from tminlp - bonmin.initialize(GetRawPtr(tminlp)); - - //Set up done, now let's branch and bound - try { - Bab bb; - bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc - } - catch(TNLPSolver::UnsolvedError *E) { - } - catch(OsiTMINLPInterface::SimpleError &E) { - } - catch(CoinError &E) { - } - rstatus=tminlp->returnStatus(); - - if(rstatus==0 ||rstatus== 3) - { - fX = tminlp->getX(); - ObjVal = tminlp->getObjVal(); - if (returnDoubleMatrixToScilab(1, nVars, 1, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - else - { - if (returnDoubleMatrixToScilab(1, 0, 0, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - - return 0; - } -} - diff --git a/newstructure/sci_gateway/cpp/cpp_intfminunc.cpp b/newstructure/sci_gateway/cpp/cpp_intfminunc.cpp deleted file mode 100644 index 3e1abcd..0000000 --- a/newstructure/sci_gateway/cpp/cpp_intfminunc.cpp +++ /dev/null @@ -1,174 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#include "CoinPragma.hpp" -#include "CoinTime.hpp" -#include "CoinError.hpp" - -#include "BonOsiTMINLPInterface.hpp" -#include "BonIpoptSolver.hpp" -#include "minuncTMINLP.hpp" -#include "BonCbc.hpp" -#include "BonBonminSetup.hpp" - -#include "BonOACutGenerator2.hpp" -#include "BonEcpCuts.hpp" -#include "BonOaNlpOptim.hpp" - -#include "sci_iofunc.hpp" -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - -int cpp_intfminunc(char *fname) -{ - using namespace Ipopt; - using namespace Bonmin; - - CheckInputArgument(pvApiCtx, 8, 8); - CheckOutputArgument(pvApiCtx, 3, 3); // 3 output arguments - - //Function pointers, input matrix(Starting point) pointer, flag variable - int* funptr=NULL; - double* x0ptr=NULL; - - // Input arguments - Number *integertolerance=NULL, *maxnodes=NULL, *allowablegap=NULL, *cputime=NULL,*max_iter=NULL; - static unsigned int nVars = 0,nCons = 0; - unsigned int temp1 = 0,temp2 = 0, iret = 0; - int x0_rows, x0_cols; - double *intcon = NULL,*options=NULL, *ifval=NULL; - int intconSize; - - // Output arguments - double *fX = NULL, ObjVal=0,iteration=0,cpuTime=0,fobj_eval=0; - double dual_inf, constr_viol, complementarity, kkt_error; - int rstatus = 0; - int int_fobj_eval, int_constr_eval, int_fobj_grad_eval, int_constr_jac_eval, int_hess_eval; - - //x0(starting point) matrix from scilab - if(getDoubleMatrixFromScilab(4, &x0_rows, &x0_cols, &x0ptr)) - { - return 1; - } - - nVars=x0_rows; - - // Getting intcon - if (getDoubleMatrixFromScilab(5,&intconSize,&temp2,&intcon)) - { - return 1; - } - - temp1 = 1; - temp2 = 1; - - //Getting parameters - if (getFixedSizeDoubleMatrixInList(6,2,temp1,temp2,&integertolerance)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(6,4,temp1,temp2,&maxnodes)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(6,6,temp1,temp2,&cputime)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(6,8,temp1,temp2,&allowablegap)) - { - return 1; - } - if (getFixedSizeDoubleMatrixInList(6,10,temp1,temp2,&max_iter)) - { - return 1; - } - - SmartPtr<minuncTMINLP> tminlp = new minuncTMINLP(nVars, x0ptr, intconSize, intcon); - - BonminSetup bonmin; - bonmin.initializeOptionsAndJournalist(); - - // Here we can change the default value of some Bonmin or Ipopt option - bonmin.options()->SetStringValue("mu_oracle","loqo"); - bonmin.options()->SetNumericValue("bonmin.integer_tolerance", *integertolerance); - bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)*maxnodes); - bonmin.options()->SetNumericValue("bonmin.time_limit", *cputime); - bonmin.options()->SetNumericValue("bonmin.allowable_gap", *allowablegap); - bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)*max_iter); - - //Now initialize from tminlp - bonmin.initialize(GetRawPtr(tminlp)); - - //Set up done, now let's branch and bound - try { - Bab bb; - bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc - } - catch(TNLPSolver::UnsolvedError *E) { - //There has been a failure to solve a problem with Ipopt. - Scierror(999, "\nIpopt has failed to solve the problem!\n"); - } - catch(OsiTMINLPInterface::SimpleError &E) { - Scierror(999, "\nFailed to solve a problem!\n"); - } - catch(CoinError &E) { - Scierror(999, "\nFailed to solve a problem!\n"); - } - rstatus=tminlp->returnStatus(); - if(rstatus==0 ||rstatus== 3) - { - fX = tminlp->getX(); - ObjVal = tminlp->getObjVal(); - if (returnDoubleMatrixToScilab(1, nVars, 1, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - else - { - if (returnDoubleMatrixToScilab(1, 0, 0, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - } - - return 0; - } -} - diff --git a/newstructure/sci_gateway/cpp/cpp_intqpipopt.cpp b/newstructure/sci_gateway/cpp/cpp_intqpipopt.cpp deleted file mode 100644 index d89d643..0000000 --- a/newstructure/sci_gateway/cpp/cpp_intqpipopt.cpp +++ /dev/null @@ -1,267 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#include <iomanip> -#include <fstream> -#include <iostream> -#include "CoinPragma.hpp" -#include "CoinTime.hpp" -#include "CoinError.hpp" - -#include "BonOsiTMINLPInterface.hpp" -#include "BonIpoptSolver.hpp" -#include "QuadTMINLP.hpp" -#include "BonCbc.hpp" -#include "BonBonminSetup.hpp" - -#include "BonOACutGenerator2.hpp" -#include "BonEcpCuts.hpp" -#include "BonOaNlpOptim.hpp" - -#include "sci_iofunc.hpp" -extern "C" -{ -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> - -int cpp_intqpipopt(char *fname) -{ - using namespace Ipopt; - using namespace Bonmin; - - CheckInputArgument(pvApiCtx, 15, 15); // We need total 15 input arguments. - CheckOutputArgument(pvApiCtx, 3, 3); // 3 output arguments - - // Error management variable - SciErr sciErr; - - // Input arguments - double *QItems=NULL,*PItems=NULL, *intcon = NULL, *ConItems=NULL,*conUB=NULL,*conLB=NULL; - double *varUB=NULL,*varLB=NULL,*init_guess=NULL,*options=NULL, *ifval=NULL; - static unsigned int nVars = 0,nCons = 0, intconSize = 0; - unsigned int temp1 = 0,temp2 = 0; - char *bonmin_options_file = NULL; - // Output arguments - double *fX = NULL, ObjVal = 0,iteration=0; - int rstatus = 0; - - //Number of Variables - if(getIntFromScilab(1,&nVars)) - { - return 1; - } - - //Number of Constraints - if (getIntFromScilab(2,&nCons)) - { - return 1; - } - - //Number of variables constrained to be integers - if (getIntFromScilab(3,&intconSize)) - { - return 1; - } - - //Q matrix from scilab - temp1 = nVars; - temp2 = nVars; - if (getFixedSizeDoubleMatrixFromScilab(4,temp1,temp1,&QItems)) - { - return 1; - } - - //P matrix from scilab - temp1 = 1; - temp2 = nVars; - if (getFixedSizeDoubleMatrixFromScilab(5,temp1,temp2,&PItems)) - { - return 1; - } - - temp1 = 1; - temp2 = intconSize; - // Getting intcon - if (getDoubleMatrixFromScilab(6,&temp1,&temp2,&intcon)) - { - return 1; - } - - if (nCons!=0) - { - //conMatrix matrix from scilab - temp1 = nCons; - temp2 = nVars; - - if (getFixedSizeDoubleMatrixFromScilab(7,temp1,temp2,&ConItems)) - { - return 1; - } - - //conLB matrix from scilab - temp1 = 1; - temp2 = nCons; - if (getFixedSizeDoubleMatrixFromScilab(8,temp1,temp2,&conLB)) - { - return 1; - } - - //conUB matrix from scilab - if (getFixedSizeDoubleMatrixFromScilab(9,temp1,temp2,&conUB)) - { - return 1; - } - } - - //varLB matrix from scilab - temp1 = 1; - temp2 = nVars; - if (getFixedSizeDoubleMatrixFromScilab(10,temp1,temp2,&varLB)) - { - return 1; - } - - //varUB matrix from scilab - if (getFixedSizeDoubleMatrixFromScilab(11,temp1,temp2,&varUB)) - { - return 1; - } - - //Initial Value of variables from scilab - if (getFixedSizeDoubleMatrixFromScilab( 12,temp1,temp2,&init_guess)) - { - return 1; - } - - temp1=1; - temp2=5; - if (getFixedSizeDoubleMatrixFromScilab(13,temp1,temp2,&options)) - { - return 1; - } - - temp1=1; - temp2=5; - if (getFixedSizeDoubleMatrixFromScilab(14,temp1,temp2,&ifval)) - { - return 1; - } - - if (getStringFromScilab(15, &bonmin_options_file)) - { - return 1; - } - - - - - SmartPtr<QuadTMINLP> tminlp = new QuadTMINLP(nVars,nCons,intconSize,QItems, PItems, intcon,ConItems,conLB,conUB,varLB,varUB,init_guess); - - BonminSetup bonmin; - bonmin.initializeOptionsAndJournalist(); - - // Here we can change the default value of some Bonmin or Ipopt option - bonmin.options()->SetStringValue("mu_oracle","loqo"); - - - //Register an additional option - if((int)ifval[0]) - bonmin.options()->SetNumericValue("bonmin.integer_tolerance", (options[0])); - if((int)ifval[1]) - bonmin.options()->SetIntegerValue("bonmin.node_limit", (int)(options[1])); - if((int)ifval[2]) - bonmin.options()->SetNumericValue("bonmin.time_limit", (options[2])); - if((int)ifval[3]) - bonmin.options()->SetNumericValue("bonmin.allowable_gap", (options[3])); - if((int)ifval[4]) - bonmin.options()->SetIntegerValue("bonmin.iteration_limit", (int)(options[4])); - - - //Here we read the option file - //if ( bonmin_options_file!=NULL ) - // bonmin.readOptionsFile(bonmin_options_file); - - //Now initialize from tminlp - bonmin.initialize(GetRawPtr(tminlp)); - - //Set up done, now let's branch and bound - try { - Bab bb; - bb(bonmin);//process parameter file using Ipopt and do branch and bound using Cbc - } - catch(TNLPSolver::UnsolvedError *E) { - //There has been a failure to solve a problem with Ipopt. - std::cerr<<"Ipopt has failed to solve a problem!"<<std::endl; - sciprint(999, "\nIpopt has failed to solve the problem!\n"); - } - catch(OsiTMINLPInterface::SimpleError &E) { - std::cerr<<E.className()<<"::"<<E.methodName() - <<std::endl - <<E.message()<<std::endl; - sciprint(999, "\nFailed to solve a problem!\n"); - } - catch(CoinError &E) { - std::cerr<<E.className()<<"::"<<E.methodName() - <<std::endl - <<E.message()<<std::endl; - sciprint(999, "\nFailed to solve a problem!\n"); - } - rstatus=tminlp->returnStatus(); - if (rstatus >= 0 | rstatus <= 5){ - fX = tminlp->getX(); - ObjVal = tminlp->getObjVal(); - if (returnDoubleMatrixToScilab(1, 1, nVars, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - } - else - { - if (returnDoubleMatrixToScilab(1, 0, 0, fX)) - { - return 1; - } - - if (returnDoubleMatrixToScilab(2, 1, 1, &ObjVal)) - { - return 1; - } - - if (returnIntegerMatrixToScilab(3, 1, 1, &rstatus)) - { - return 1; - } - - sciprint(999, "\nThe problem could not be solved!\n"); - } - - // As the SmartPtrs go out of scope, the reference count - // will be decremented and the objects will automatically - // be deleted(No memory leakage). - - return 0; -} -} - diff --git a/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.c b/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.c deleted file mode 100644 index 7090628..0000000 --- a/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.c +++ /dev/null @@ -1,40 +0,0 @@ -#ifdef __cplusplus -extern "C" { -#endif -#include <mex.h> -#include <sci_gateway.h> -#include <api_scilab.h> -#include <MALLOC.h> -static int direct_gateway(char *fname,void F(void)) { F();return 0;}; -extern Gatefunc cpp_intfminunc; -extern Gatefunc cpp_intfminbnd; -extern Gatefunc cpp_intfmincon; -extern Gatefunc cpp_intqpipopt; -extern Gatefunc matrix_cppintlinprog; -extern Gatefunc mps_cppintlinprog; -static GenericTable Tab[]={ - {(Myinterfun)sci_gateway,cpp_intfminunc,"inter_fminunc"}, - {(Myinterfun)sci_gateway,cpp_intfminbnd,"inter_fminbnd"}, - {(Myinterfun)sci_gateway,cpp_intfmincon,"inter_fmincon"}, - {(Myinterfun)sci_gateway,cpp_intqpipopt,"sci_intqpipopt"}, - {(Myinterfun)sci_gateway,matrix_cppintlinprog,"sci_matrix_intlinprog"}, - {(Myinterfun)sci_gateway,mps_cppintlinprog,"sci_mps_intlinprog"}, -}; - -int C2F(libFOSSEE_Optimization_Toolbox)() -{ - Rhs = Max(0, Rhs); - if (*(Tab[Fin-1].f) != NULL) - { - if(pvApiCtx == NULL) - { - pvApiCtx = (StrCtx*)MALLOC(sizeof(StrCtx)); - } - pvApiCtx->pstName = (char*)Tab[Fin-1].name; - (*(Tab[Fin-1].f))(Tab[Fin-1].name,Tab[Fin-1].F); - } - return 0; -} -#ifdef __cplusplus -} -#endif diff --git a/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.so b/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.so Binary files differdeleted file mode 100755 index 233098e..0000000 --- a/newstructure/sci_gateway/cpp/libFOSSEE_Optimization_Toolbox.so +++ /dev/null diff --git a/newstructure/sci_gateway/cpp/loader.sce b/newstructure/sci_gateway/cpp/loader.sce deleted file mode 100644 index cad0490..0000000 --- a/newstructure/sci_gateway/cpp/loader.sce +++ /dev/null @@ -1,26 +0,0 @@ -// This file is released under the 3-clause BSD license. See COPYING-BSD. -// Generated by builder.sce : Please, do not edit this file -// ---------------------------------------------------------------------------- -// -libFOSSEE_Optimizat_path = get_absolute_file_path('loader.sce'); -// -// ulink previous function with same name -[bOK, ilib] = c_link('libFOSSEE_Optimization_Toolbox'); -if bOK then - ulink(ilib); -end -// -list_functions = [ 'inter_fminunc'; - 'inter_fminbnd'; - 'inter_fmincon'; - 'sci_intqpipopt'; - 'sci_matrix_intlinprog'; - 'sci_mps_intlinprog'; -]; -addinter(libFOSSEE_Optimizat_path + filesep() + 'libFOSSEE_Optimization_Toolbox' + getdynlibext(), 'libFOSSEE_Optimization_Toolbox', list_functions); -// remove temp. variables on stack -clear libFOSSEE_Optimizat_path; -clear bOK; -clear ilib; -clear list_functions; -// ---------------------------------------------------------------------------- diff --git a/newstructure/sci_gateway/cpp/minbndTMINLP.hpp b/newstructure/sci_gateway/cpp/minbndTMINLP.hpp deleted file mode 100644 index 581d5ce..0000000 --- a/newstructure/sci_gateway/cpp/minbndTMINLP.hpp +++ /dev/null @@ -1,114 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#ifndef minbndTMINLP_HPP -#define minbndTMINLP_HPP - -#include "BonTMINLP.hpp" -#include "IpTNLP.hpp" -#include "call_scilab.h" - -using namespace Ipopt; -using namespace Bonmin; - -class minbndTMINLP : public TMINLP -{ - private: - - Index numVars_; //Number of input variables - - Index intconSize_; - - Number *lb_= NULL; //lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables. - - Number *ub_= NULL; //ub_ is a pointer to a matrix of size of 1*numVars_ with upper bound of all variables. - - Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables. - - Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. - - Number *intcon_ = NULL; - - int status_; //Solver return status - minbndTMINLP(const minbndTMINLP&); - minbndTMINLP& operator=(const minbndTMINLP&); - -public: - // Constructor - minbndTMINLP(Index nV, Number *lb, Number *ub, Index intconSize, Number *intcon):numVars_(nV),lb_(lb),ub_(ub),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){ } - - /** default destructor */ - virtual ~minbndTMINLP(); - - virtual bool get_variables_types(Index n, VariableType* var_types); - - virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types); - - virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types); - - /** Method to return some info about the nlp */ - virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, - Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style); - - /** Method to return the bounds for my problem */ - virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, - Index m, Number* g_l, Number* g_u); - - /** Method to return the starting point for the algorithm */ - virtual bool get_starting_point(Index n, bool init_x, Number* x, - bool init_z, Number* z_L, Number* z_U, - Index m, bool init_lambda, - Number* lambda); - - /** Method to return the objective value */ - virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); - - /** Method to return the gradient of the objective */ - virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); - - /** Method to return the constraint residuals */ - virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); - - /** Method to return: - * 1) The structure of the jacobian (if "values" is NULL) - * 2) The values of the jacobian (if "values" is not NULL) - */ - virtual bool eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values); - - /** Method to return: - * 1) The structure of the hessian of the lagrangian (if "values" is NULL) - * 2) The values of the hessian of the lagrangian (if "values" is not NULL) - */ - virtual bool eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values); - - /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ - virtual void finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value); - - virtual const SosInfo * sosConstraints() const{return NULL;} - virtual const BranchingInfo* branchingInfo() const{return NULL;} - - const double * getX(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value for the primal variables. - - const double * getGrad(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value of gradient for the primal variables. - - const double * getHess(); //Returns a pointer to a matrix of size of numVars_*numVars_ - //with final value of hessian for the primal variables. - - double getObjVal(); //Returns the output of the final value of the objective. - - double iterCount(); //Returns the iteration count - - int returnStatus(); //Returns the status count -}; - -#endif diff --git a/newstructure/sci_gateway/cpp/minconTMINLP.hpp b/newstructure/sci_gateway/cpp/minconTMINLP.hpp deleted file mode 100644 index 5b3006a..0000000 --- a/newstructure/sci_gateway/cpp/minconTMINLP.hpp +++ /dev/null @@ -1,124 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#ifndef minconTMINLP_HPP -#define minconTMINLP_HPP - -#include "BonTMINLP.hpp" -#include "IpTNLP.hpp" -#include "call_scilab.h" - -using namespace Ipopt; -using namespace Bonmin; - -class minconTMINLP : public TMINLP -{ - private: - - Index numVars_; //Number of variables - - Index numCons_; //Number of constraints - - Index numLC_; //Number of Linear constraints - - Index intconSize_; - - Number *x0_= NULL; //lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables. - - Number *lb_= NULL; //lb_ is a pointer to a matrix of size of 1*numVars_ with lower bound of all variables. - - Number *ub_= NULL; //ub_ is a pointer to a matrix of size of 1*numVars_ with upper bound of all variables. - - Number *conLb_= NULL; //conLb_ is a pointer to a matrix of size of numCon_*1 with lower bound of all constraints. - - Number *conUb_= NULL; //conUb_ is a pointer to a matrix of size of numCon_*1 with upper bound of all constraints. - - Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables. - - Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. - - Number *intcon_ = NULL; - - int status_; //Solver return status - minconTMINLP(const minconTMINLP&); - minconTMINLP& operator=(const minconTMINLP&); - -public: - // Constructor - minconTMINLP(Index nV, Number *x0, Number *lb, Number *ub, Index nLC, Index nCons, Number *conlb, Number *conub, Index intconSize, Number *intcon):numVars_(nV),x0_(x0),lb_(lb),ub_(ub),numLC_(nLC),numCons_(nCons),conLb_(conlb),conUb_(conub),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){ } - - /** default destructor */ - virtual ~minconTMINLP(); - - virtual bool get_variables_types(Index n, VariableType* var_types); - - virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types); - - virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types); - - /** Method to return some info about the nlp */ - virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, - Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style); - - /** Method to return the bounds for my problem */ - virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, - Index m, Number* g_l, Number* g_u); - - /** Method to return the starting point for the algorithm */ - virtual bool get_starting_point(Index n, bool init_x, Number* x, - bool init_z, Number* z_L, Number* z_U, - Index m, bool init_lambda, - Number* lambda); - - /** Method to return the objective value */ - virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); - - /** Method to return the gradient of the objective */ - virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); - - /** Method to return the constraint residuals */ - virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); - - /** Method to return: - * 1) The structure of the jacobian (if "values" is NULL) - * 2) The values of the jacobian (if "values" is not NULL) - */ - virtual bool eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values); - - /** Method to return: - * 1) The structure of the hessian of the lagrangian (if "values" is NULL) - * 2) The values of the hessian of the lagrangian (if "values" is not NULL) - */ - virtual bool eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values); - - /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ - virtual void finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value); - - virtual const SosInfo * sosConstraints() const{return NULL;} - virtual const BranchingInfo* branchingInfo() const{return NULL;} - - const double * getX(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value for the primal variables. - - const double * getGrad(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value of gradient for the primal variables. - - const double * getHess(); //Returns a pointer to a matrix of size of numVars_*numVars_ - //with final value of hessian for the primal variables. - - double getObjVal(); //Returns the output of the final value of the objective. - - double iterCount(); //Returns the iteration count - - int returnStatus(); //Returns the status count -}; - -#endif diff --git a/newstructure/sci_gateway/cpp/minuncTMINLP.hpp b/newstructure/sci_gateway/cpp/minuncTMINLP.hpp deleted file mode 100644 index 2b6e954..0000000 --- a/newstructure/sci_gateway/cpp/minuncTMINLP.hpp +++ /dev/null @@ -1,113 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#define __USE_DEPRECATED_STACK_FUNCTIONS__ -#ifndef minuncTMINLP_HPP -#define minuncTMINLP_HPP - -#include "BonTMINLP.hpp" -#include "IpTNLP.hpp" -#include "call_scilab.h" - -using namespace Ipopt; -using namespace Bonmin; - -class minuncTMINLP : public TMINLP -{ - private: - - Index numVars_; //Number of input variables - - Index intconSize_; - - const Number *varGuess_= NULL; //varGuess_ is a pointer to a matrix of size of 1*numVars_ with initial guess of all variables. - - Number *finalX_= NULL; //finalX_ is a pointer to a matrix of size of 1*numVars_ with final value for the primal variables. - - Number finalObjVal_; //finalObjVal_ is a scalar with the final value of the objective. - - Number *intcon_ = NULL; - - int status_; //Solver return status - minuncTMINLP(const minuncTMINLP&); - minuncTMINLP& operator=(const minuncTMINLP&); - -public: - // Constructor - minuncTMINLP(Index nV, Number *x0, Index intconSize, Number *intcon):numVars_(nV),varGuess_(x0),intconSize_(intconSize),intcon_(intcon),finalX_(0),finalObjVal_(1e20){ } - - /** default destructor */ - virtual ~minuncTMINLP(); - - virtual bool get_variables_types(Index n, VariableType* var_types); - - virtual bool get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types); - - virtual bool get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types); - - /** Method to return some info about the nlp */ - virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, - Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style); - - /** Method to return the bounds for my problem */ - virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, - Index m, Number* g_l, Number* g_u); - - /** Method to return the starting point for the algorithm */ - virtual bool get_starting_point(Index n, bool init_x, Number* x, - bool init_z, Number* z_L, Number* z_U, - Index m, bool init_lambda, - Number* lambda); - - /** Method to return the objective value */ - virtual bool eval_f(Index n, const Number* x, bool new_x, Number& obj_value); - - /** Method to return the gradient of the objective */ - virtual bool eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f); - - /** Method to return the constraint residuals */ - virtual bool eval_g(Index n, const Number* x, bool new_x, Index m, Number* g); - - /** Method to return: - * 1) The structure of the jacobian (if "values" is NULL) - * 2) The values of the jacobian (if "values" is not NULL) - */ - virtual bool eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values); - - /** Method to return: - * 1) The structure of the hessian of the lagrangian (if "values" is NULL) - * 2) The values of the hessian of the lagrangian (if "values" is not NULL) - */ - virtual bool eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values); - - /** This method is called when the algorithm is complete so the TNLP can store/write the solution */ - virtual void finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value); - - virtual const SosInfo * sosConstraints() const{return NULL;} - virtual const BranchingInfo* branchingInfo() const{return NULL;} - - const double * getX(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value for the primal variables. - - const double * getGrad(); //Returns a pointer to a matrix of size of 1*numVars_ - //with final value of gradient for the primal variables. - - const double * getHess(); //Returns a pointer to a matrix of size of numVars_*numVars_ - //with final value of hessian for the primal variables. - - double getObjVal(); //Returns the output of the final value of the objective. - - double iterCount(); //Returns the iteration count - - int returnStatus(); //Returns the status count -}; - -#endif diff --git a/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp deleted file mode 100644 index a424b47..0000000 --- a/newstructure/sci_gateway/cpp/sci_QuadTMINLP.cpp +++ /dev/null @@ -1,230 +0,0 @@ -// Copyright (C) 2016 - IIT Bombay - FOSSEE -// -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in - -#include "QuadTMINLP.hpp" -#include "IpIpoptData.hpp" - -extern "C"{ -#include <sciprint.h> -} - -// Go to http://coin-or.org/Ipopt and http://coin-or.org/Bonmin for the details of the below methods - -// Set the type of every variable - CONTINUOUS or INTEGER -bool QuadTMINLP::get_variables_types(Index n, VariableType* var_types) -{ - n = numVars_; - for(int i=0; i < n; i++) - var_types[i] = CONTINUOUS; - for(int i=0 ; i < intconSize_ ; ++i) - var_types[(int)(intcon_[i]-1)] = INTEGER; - return true; -} - -// The linearity of the variables - LINEAR or NON_LINEAR -bool QuadTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types) -{ - /* - n = numVars_; - for(int i = 0; i < n; i++) - var_types[i] = Ipopt::TNLP::LINEAR; - */ - return true; -} - -// The linearity of the constraints - LINEAR or NON_LINEAR -bool QuadTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types) -{ - m = numCons_; - for(int i = 0; i < m; i++) - const_types[i] = Ipopt::TNLP::LINEAR; - return true; -} - -// Get MINLP info such as the number of variables,constraints,no.of elements in jacobian and hessian to allocate memory -bool QuadTMINLP::get_nlp_info(Index& n, Index&m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style) -{ - n=numVars_; // Number of variables - m=numCons_; // Number of constraints - nnz_jac_g = n*m; // No. of elements in Jacobian of constraints - nnz_h_lag = (n*(n+1))/2; // No. of elements in lower traingle of Hessian of the Lagrangian. - index_style=TNLP::C_STYLE; // Index style of matrices - return true; -} - -// Get the variables and constraints bound info -bool QuadTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u) -{ - unsigned int i; - for(i=0;i<n;i++){ - x_l[i]=varLB_[i]; - x_u[i]=varUB_[i]; - } - - for(i=0;i<m;i++){ - g_l[i]=conLB_[i]; - g_u[i]=conUB_[i]; - } - return true; -} - -// This method sets initial values for all the required vectors. We take 0 by default. -bool QuadTMINLP::get_starting_point(Index n, bool init_x, Number* x, - bool init_z, Number* z_L, Number* z_U, - Index m, bool init_lambda, - Number* lambda) -{ - if (init_x == true){ //we need to set initial values for vector x - for (Index var=0;var<n;var++) - x[var]=varGuess_[var];//initialize with 0 or we can change. - } - - if (init_z == true){ //we need to provide initial values for vector bound multipliers - for (Index var=0;var<n;++var){ - z_L[var]=0.0; //initialize with 0 or we can change. - z_U[var]=0.0;//initialize with 0 or we can change. - } - } - - if (init_lambda == true){ //we need to provide initial values for lambda values. - for (Index var=0;var<m;++var){ - lambda[var]=0.0; //initialize with 0 or we can change. - } - } - - return true; -} - -// Evaluate the objective function at a point -bool QuadTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value) -{ - unsigned int i,j; - obj_value=0; - for (i=0;i<n;i++){ - for (j=0;j<n;j++){ - obj_value+=0.5*x[i]*x[j]*qMatrix_[n*i+j]; - } - obj_value+=x[i]*lMatrix_[i]; - } - return true; -} - -// Get the value of gradient of objective function at vector x. -bool QuadTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f) -{ - unsigned int i,j; - for(i=0;i<n;i++) - { - grad_f[i]=lMatrix_[i]; - for(j=0;j<n;j++) - { - grad_f[i]+=(qMatrix_[n*i+j])*x[j]; - } - } - return true; -} - -// Get the values of constraints at vector x. -bool QuadTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g) -{ - unsigned int i,j; - for(i=0;i<m;i++) - { - g[i]=0; - for(j=0;j<n;j++) - { - g[i]+=x[j]*conMatrix_[i+j*m]; - } - } - return true; -} - -// The Jacobian Matrix -bool QuadTMINLP::eval_jac_g(Index n, const Number* x, bool new_x, - Index m, Index nnz_jac, Index* iRow, Index *jCol, - Number* values) -{ - //It asks for the structure of the jacobian. - if (values==NULL){ //Structure of jacobian (full structure) - int index=0; - for (int var=0;var<m;++var)//no. of constraints - for (int flag=0;flag<n;++flag){//no. of variables - iRow[index]=var; - jCol[index]=flag; - index++; - } - } - //It asks for values - else { - int index=0; - for (int var=0;var<m;++var) - for (int flag=0;flag<n;++flag) - values[index++]=conMatrix_[var+flag*m]; - } - return true; -} - -/* -The structure of the Hessain matrix and the values -*/ -bool QuadTMINLP::eval_h(Index n, const Number* x, bool new_x, - Number obj_factor, Index m, const Number* lambda, - bool new_lambda, Index nele_hess, Index* iRow, - Index* jCol, Number* values) -{ - if (values==NULL){ - Index idx=0; - for (Index row = 0; row < n; row++) { - for (Index col = 0; col <= row; col++) { - iRow[idx] = row; - jCol[idx] = col; - idx++; - } - } - } - else { - Index index=0; - for (Index row=0;row < n;++row){ - for (Index col=0; col <= row; ++col){ - values[index++]=obj_factor*(qMatrix_[n*row+col]); - } - } - } - return true; -} - -void QuadTMINLP::finalize_solution(TMINLP::SolverReturn status, Index n, const Number* x,Number obj_value) -{ - - finalX_ = (double*)malloc(sizeof(double) * numVars_ * 1); - for (Index i=0; i<n; i++) - { - finalX_[i] = x[i]; - } - - finalObjVal_ = obj_value; - status_ = status; -} - -const double * QuadTMINLP::getX() -{ - return finalX_; -} - -double QuadTMINLP::getObjVal() -{ - return finalObjVal_; -} - -int QuadTMINLP::returnStatus() -{ - return status_; -} diff --git a/newstructure/sci_gateway/cpp/sci_iofunc.cpp b/newstructure/sci_gateway/cpp/sci_iofunc.cpp deleted file mode 100644 index f05839c..0000000 --- a/newstructure/sci_gateway/cpp/sci_iofunc.cpp +++ /dev/null @@ -1,333 +0,0 @@ -// Symphony Toolbox for Scilab -// (Definition of) Functions for input and output from Scilab -// By Keyur Joshi - -#include "api_scilab.h" -#include "Scierror.h" -#include "sciprint.h" -#include "BOOL.h" -#include <localization.h> -#include "call_scilab.h" -#include <string.h> - - -using namespace std; - -int getDoubleFromScilab(int argNum, double *dest) -{ - //data declarations - SciErr sciErr; - int iRet,*varAddress; - const char errMsg[]="Wrong type for input argument #%d: A double is expected.\n"; - const int errNum=999; - //get variable address - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - //check that it is a non-complex double - if ( !isDoubleType(pvApiCtx,varAddress) || isVarComplex(pvApiCtx,varAddress) ) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - //retrieve and store - iRet = getScalarDouble(pvApiCtx, varAddress, dest); - if(iRet) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - return 0; -} - -int getUIntFromScilab(int argNum, int *dest) -{ - SciErr sciErr; - int iRet,*varAddress; - double inputDouble; - const char errMsg[]="Wrong type for input argument #%d: A nonnegative integer is expected.\n"; - const int errNum=999; - //same steps as above - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - if ( !isDoubleType(pvApiCtx,varAddress) || isVarComplex(pvApiCtx,varAddress) ) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - iRet = getScalarDouble(pvApiCtx, varAddress, &inputDouble); - //check that an unsigned int is stored in the double by casting and recasting - if(iRet || ((double)((unsigned int)inputDouble))!=inputDouble) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - *dest=(unsigned int)inputDouble; - return 0; -} - -int getIntFromScilab(int argNum, int *dest) -{ - SciErr sciErr; - int iRet,*varAddress; - double inputDouble; - const char errMsg[]="Wrong type for input argument #%d: An integer is expected.\n"; - const int errNum=999; - //same steps as above - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - if ( !isDoubleType(pvApiCtx,varAddress) || isVarComplex(pvApiCtx,varAddress) ) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - iRet = getScalarDouble(pvApiCtx, varAddress, &inputDouble); - //check that an int is stored in the double by casting and recasting - if(iRet || ((double)((int)inputDouble))!=inputDouble) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - *dest=(int)inputDouble; - return 0; -} - -int getFixedSizeDoubleMatrixFromScilab(int argNum, int rows, int cols, double **dest) -{ - int *varAddress,inputMatrixRows,inputMatrixCols; - SciErr sciErr; - const char errMsg[]="Wrong type for input argument #%d: A matrix of double of size %d by %d is expected.\n"; - const int errNum=999; - //same steps as above - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - if ( !isDoubleType(pvApiCtx,varAddress) || isVarComplex(pvApiCtx,varAddress) ) - { - Scierror(errNum,errMsg,argNum,rows,cols); - return 1; - } - sciErr = getMatrixOfDouble(pvApiCtx, varAddress, &inputMatrixRows, &inputMatrixCols,NULL); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - //check that the matrix has the correct number of rows and columns - if(inputMatrixRows!=rows || inputMatrixCols!=cols) - { - Scierror(errNum,errMsg,argNum,rows,cols); - return 1; - } - getMatrixOfDouble(pvApiCtx, varAddress, &inputMatrixRows, &inputMatrixCols, dest); - return 0; -} - -int getDoubleMatrixFromScilab(int argNum, int *rows, int *cols, double **dest) -{ - int *varAddress; - SciErr sciErr; - const char errMsg[]="Wrong type for input argument #%d: A matrix of double is expected.\n"; - const int errNum=999; - //same steps as above - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - if ( !isDoubleType(pvApiCtx,varAddress) || isVarComplex(pvApiCtx,varAddress) ) - { - Scierror(errNum,errMsg,argNum); - return 1; - } - getMatrixOfDouble(pvApiCtx, varAddress, rows, cols, dest); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - return 0; -} - -int getFixedSizeDoubleMatrixInList(int argNum, int itemPos, int rows, int cols, double **dest) -{ - int *varAddress,inputMatrixRows,inputMatrixCols; - SciErr sciErr; - const char errMsg[]="Wrong type for input argument #%d: A matrix of double of size %d by %d is expected.\n"; - const int errNum=999; - //same steps as above - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - - getMatrixOfDoubleInList(pvApiCtx, varAddress, itemPos, &rows, &cols, dest); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - return 0; -} - -int getStringFromScilab(int argNum,char **dest) -{ - int *varAddress,inputMatrixRows,inputMatrixCols; - SciErr sciErr; - sciErr = getVarAddressFromPosition(pvApiCtx, argNum, &varAddress); - - //check whether there is an error or not. - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - if ( !isStringType(pvApiCtx,varAddress) ) - { - Scierror(999,"Wrong type for input argument 1: A file name is expected.\n"); - return 1; - } - //read the value in that pointer pointing to file name - getAllocatedSingleString(pvApiCtx, varAddress, dest); - -} - -bool getFunctionFromScilab(int n,char name[], double *x,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest) -{ - double check; - createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF, 1, n, x); - C2F(scistring)(&posFirstElementOnStackForSF,name,&nOfLhsOnSF,&nOfRhsOnSF,(unsigned long)strlen(name)); - - if(getDoubleFromScilab(posFirstElementOnStackForSF+1,&check)) - { - return true; - } - if (check==1) - { - return true; - } - else - { - int x_rows, x_cols; - if(getDoubleMatrixFromScilab(posFirstElementOnStackForSF, &x_rows, &x_cols, dest)) - { - sciprint("No results "); - return true; - - } - } - return 0; -} - -bool getHessFromScilab(int n,int numConstr_,char name[], double *x,double *obj,double *lambda,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest) -{ - double check; - createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF, 1, n, x); - createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF+1, 1, 1, obj); - createMatrixOfDouble(pvApiCtx, posFirstElementOnStackForSF+2, 1, numConstr_, lambda); - C2F(scistring)(&posFirstElementOnStackForSF,name,&nOfLhsOnSF,&nOfRhsOnSF,(unsigned long)strlen(name)); - - if(getDoubleFromScilab(posFirstElementOnStackForSF+1,&check)) - { - return true; - } - if (check==1) - { - return true; - } - else - { - int x_rows, x_cols; - if(getDoubleMatrixFromScilab(posFirstElementOnStackForSF, &x_rows, &x_cols, dest)) - { - sciprint("No results "); - return 1; - } - } - return 0; -} - -int return0toScilab() -{ - int iRet; - //create variable in scilab - iRet = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1,0); - if(iRet) - { - /* If error, no return variable */ - AssignOutputVariable(pvApiCtx, 1) = 0; - return 1; - } - //make it the output variable - AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; - //return it to scilab - //ReturnArguments(pvApiCtx); - return 0; -} - -int returnDoubleToScilab(double retVal) -{ - int iRet; - //same steps as above - iRet = createScalarDouble(pvApiCtx, nbInputArgument(pvApiCtx)+1,retVal); - if(iRet) - { - /* If error, no return variable */ - AssignOutputVariable(pvApiCtx, 1) = 0; - return 1; - } - AssignOutputVariable(pvApiCtx, 1) = nbInputArgument(pvApiCtx)+1; - //ReturnArguments(pvApiCtx); - return 0; -} - -int returnDoubleMatrixToScilab(int itemPos, int rows, int cols, double *dest) -{ - SciErr sciErr; - //same steps as above - sciErr = createMatrixOfDouble(pvApiCtx, nbInputArgument(pvApiCtx) + itemPos, rows, cols, dest); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - - AssignOutputVariable(pvApiCtx, itemPos) = nbInputArgument(pvApiCtx)+itemPos; - - return 0; -} - -int returnIntegerMatrixToScilab(int itemPos, int rows, int cols, int *dest) -{ - SciErr sciErr; - //same steps as above - sciErr = createMatrixOfInteger32(pvApiCtx, nbInputArgument(pvApiCtx) + itemPos, rows, cols, dest); - if (sciErr.iErr) - { - printError(&sciErr, 0); - return 1; - } - - AssignOutputVariable(pvApiCtx, itemPos) = nbInputArgument(pvApiCtx)+itemPos; - - return 0; -} - - diff --git a/newstructure/sci_gateway/cpp/sci_iofunc.hpp b/newstructure/sci_gateway/cpp/sci_iofunc.hpp deleted file mode 100644 index 7e18951..0000000 --- a/newstructure/sci_gateway/cpp/sci_iofunc.hpp +++ /dev/null @@ -1,25 +0,0 @@ -// Symphony Toolbox for Scilab -// (Declaration of) Functions for input and output from Scilab -// By Keyur Joshi - -#ifndef SCI_IOFUNCHEADER -#define SCI_IOFUNCHEADER - -//input -int getDoubleFromScilab(int argNum, double *dest); -int getUIntFromScilab(int argNum, int *dest); -int getIntFromScilab(int argNum, int *dest); -int getFixedSizeDoubleMatrixFromScilab(int argNum, int rows, int cols, double **dest); -int getDoubleMatrixFromScilab(int argNum, int *rows, int *cols, double **dest); -int getFixedSizeDoubleMatrixInList(int argNum, int itemPos, int rows, int cols, double **dest); -int getStringFromScilab(int argNum,char** dest); -bool getFunctionFromScilab(int n,char name[], double *x,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest); -bool getHessFromScilab(int n,int numConstr_,char name[], double *x,double *obj,double *lambda,int posFirstElementOnStackForSF,int nOfRhsOnSF,int nOfLhsOnSF, double **dest); - -//output -int return0toScilab(); -int returnDoubleToScilab(double retVal); -int returnDoubleMatrixToScilab(int itemPos, int rows, int cols, double *dest); -int returnIntegerMatrixToScilab(int itemPos, int rows, int cols, int *dest); - -#endif //SCI_IOFUNCHEADER diff --git a/newstructure/sci_gateway/cpp/sci_minbndTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_minbndTMINLP.cpp deleted file mode 100644 index f26c089..0000000 --- a/newstructure/sci_gateway/cpp/sci_minbndTMINLP.cpp +++ /dev/null @@ -1,218 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -#include "minbndTMINLP.hpp" -#include "sci_iofunc.hpp" - -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> -#include <string.h> -#include <assert.h> -} - -using namespace Ipopt; -using namespace Bonmin; - -minbndTMINLP::~minbndTMINLP() -{ - if(finalX_) delete[] finalX_; -} - -// Set the type of every variable - CONTINUOUS or INTEGER -bool minbndTMINLP::get_variables_types(Index n, VariableType* var_types) -{ - n = numVars_; - for(int i=0; i < n; i++) - var_types[i] = CONTINUOUS; - for(int i=0 ; i < intconSize_ ; ++i) - var_types[(int)(intcon_[i]-1)] = INTEGER; - return true; -} - -// The linearity of the variables - LINEAR or NON_LINEAR -bool minbndTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types) -{ return true; } - -// The linearity of the constraints - LINEAR or NON_LINEAR -bool minbndTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types) -{ return true;} - -//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory -bool minbndTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style) -{ - n=numVars_; // Number of variables - m=0; // Number of constraints - nnz_jac_g = 0; // No. of elements in Jacobian of constraints - nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian. - index_style=TNLP::C_STYLE; // Index style of matrices - return true; -} - -//get variable and constraint bound info -bool minbndTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u) -{ - unsigned int i; - for(i=0;i<n;i++) - { - x_l[i]=lb_[i]+0.0000001; - x_u[i]=ub_[i]-0.0000001; - } - - g_l=NULL; - g_u=NULL; - return true; -} - -// return the value of the constraints: g(x) -bool minbndTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g) -{ - // return the value of the constraints: g(x) - g=NULL; - return true; -} - -// return the structure or values of the jacobian -bool minbndTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values) -{ - if (values == NULL) - { - // return the structure of the jacobian of the constraints - iRow=NULL; - jCol=NULL; - } - else - { - values=NULL; - } - return true; -} - -//get value of objective function at vector x -bool minbndTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value) -{ - char name[20]="_f"; - Number *obj; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj)) - { - return false; - } - obj_value = *obj; - return true; -} - -//get value of gradient of objective function at vector x. -bool minbndTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f) -{ - char name[20]="_gradf"; - Number *resg; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg)) - { - return false; - } - - Index i; - for(i=0;i<numVars_;i++) - { - grad_f[i]=resg[i]; - } - return true; -} - -// This method sets initial values for required vectors . For now we are assuming 0 to all values. -bool minbndTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_z, Number* z_L, Number* z_U,Index m, bool init_lambda,Number* lambda) -{ - assert(init_x == true); - assert(init_z == false); - assert(init_lambda == false); - if (init_x == true) - { //we need to set initial values for vector x - for (Index var=0;var<n;var++) - {x[var]=0.0;}//initialize with 0. - } - return true; -} - -/* - * Return either the sparsity structure of the Hessian of the Lagrangian, - * or the values of the Hessian of the Lagrangian for the given values for - * x,lambda,obj_factor. -*/ - -bool minbndTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values) -{ - double check; - if (values==NULL) - { - Index idx=0; - for (Index row = 0; row < numVars_; row++) - { - for (Index col = 0; col <= row; col++) - { iRow[idx] = row; - jCol[idx] = col; - idx++; - } - } - } - - else - { char name[20]="_gradhess"; - Number *resh; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&resh)) - { - return false; - } - Index index=0; - for (Index row=0;row < numVars_ ;++row) - { - for (Index col=0; col <= row; ++col) - { - values[index++]=obj_factor*(resh[numVars_*row+col]); - } - } - } - return true; -} - -void minbndTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value) -{ - finalObjVal_ = obj_value; - status_ = status; - if(status==0 ||status== 3) - { - finalX_ = new double[n]; - for (Index i=0; i<numVars_; i++) - { - finalX_[i] = x[i]; - } - } - -} - -const double * minbndTMINLP::getX() -{ - return finalX_; -} - -double minbndTMINLP::getObjVal() -{ - return finalObjVal_; -} - -int minbndTMINLP::returnStatus() -{ - return status_; -} diff --git a/newstructure/sci_gateway/cpp/sci_minconTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_minconTMINLP.cpp deleted file mode 100644 index 350594d..0000000 --- a/newstructure/sci_gateway/cpp/sci_minconTMINLP.cpp +++ /dev/null @@ -1,324 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -#include "minconTMINLP.hpp" -#include "sci_iofunc.hpp" - -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> -#include <string.h> -#include <assert.h> -} - -using namespace Ipopt; -using namespace Bonmin; - -//#define DEBUG 0 - -minconTMINLP::~minconTMINLP() -{ - if(finalX_) delete[] finalX_; -} - -// Set the type of every variable - CONTINUOUS or INTEGER -bool minconTMINLP::get_variables_types(Index n, VariableType* var_types) -{ - #ifdef DEBUG - sciprint("Code is in get_variables_types\n"); - #endif - n = numVars_; - for(int i=0; i < n; i++) - var_types[i] = CONTINUOUS; - for(int i=0 ; i < intconSize_ ; ++i) - var_types[(int)(intcon_[i]-1)] = INTEGER; - return true; -} - -// The linearity of the variables - LINEAR or NON_LINEAR -bool minconTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types) -{ - #ifdef DEBUG - sciprint("Code is in get_variables_linearity\n"); - #endif - for(int i=0;i<n;i++) - { - var_types[i] = Ipopt::TNLP::NON_LINEAR; - } - return true; } - -// The linearity of the constraints - LINEAR or NON_LINEAR -bool minconTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types) -{ - - #ifdef DEBUG - sciprint("Code is in get_constraints_linearity\n"); - #endif - for(int i=0;i<numLC_;i++) - { - const_types[i] = Ipopt::TNLP::LINEAR; - } - - for(int i=numLC_;i<m;i++) - { - const_types[i] = Ipopt::TNLP::NON_LINEAR; - } - return true;} - -//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory -bool minconTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style) -{ - #ifdef DEBUG - sciprint("Code is in get_nlp_info\n"); - #endif - n=numVars_; // Number of variables - m=numCons_; // Number of constraints - nnz_jac_g = n*m; // No. of elements in Jacobian of constraints - nnz_h_lag = n*n; // No. of elements in Hessian of the Lagrangian. - index_style=TNLP::C_STYLE; // Index style of matrices - return true; -} - -//get variable and constraint bound info -bool minconTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u) -{ - #ifdef DEBUG - sciprint("Code is in get_bounds_info\n"); - #endif - unsigned int i; - for(i=0;i<n;i++) - { - x_l[i]=lb_[i]; - x_u[i]=ub_[i]; - } - for(i=0;i<m;i++) - { - g_l[i]=conLb_[i]; - g_u[i]=conUb_[i]; - } - return true; -} - -// This method sets initial values for required vectors . For now we are assuming 0 to all values. -bool minconTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_z, Number* z_L, Number* z_U,Index m, bool init_lambda,Number* lambda) -{ - assert(init_x == true); - assert(init_z == false); - assert(init_lambda == false); - if (init_x == true) - { //we need to set initial values for vector x - for (Index var=0;var<n;var++) - {x[var]=x0_[var];}//initialize with 0. - } - return true; -} - -//get value of objective function at vector x -bool minconTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value) -{ - #ifdef DEBUG - sciprint("Code is eval_f\n"); - #endif - char name[20]="_f"; - Number *obj; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj)) - { - return false; - } - obj_value = *obj; - return true; -} - -//get value of gradient of objective function at vector x. -bool minconTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f) -{ - #ifdef DEBUG - sciprint("Code is in eval_grad_f\n"); - #endif - char name[20]="_gradf"; - Number *resg; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&resg)) - { - return false; - } - - Index i; - for(i=0;i<numVars_;i++) - { - grad_f[i]=resg[i]; - } - return true; -} - -// return the value of the constraints: g(x) -bool minconTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g) -{ - #ifdef DEBUG - sciprint("Code is in eval_g\n"); - #endif - // return the value of the constraints: g(x) - if(m==0) - { - g=NULL; - } - else - { - char name[20]="_addnlc"; - Number *con; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&con)) - { - return false; - } - - Index i; - for(i=0;i<m;i++) - { - g[i]=con[i]; - } - } - - return true; -} - -// return the structure or values of the jacobian -bool minconTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values) -{ - #ifdef DEBUG - sciprint("Code is in eval_jac_g\n"); - #endif - if (values == NULL) - { - if(m==0)// return the structure of the jacobian of the constraints - { - iRow=NULL; - jCol=NULL; - } - else - { - unsigned int i,j,idx=0; - for(i=0;i<m;i++) - for(j=0;j<n;j++) - { - iRow[idx]=i; - jCol[idx]=j; - idx++; - } - } - } - else - { - if(m==0) - { - values=NULL; - } - else - { - double* resj; - char name[20]="_gradnlc"; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&resj)) - { - return false; - } - int c = 0; - for(int i=0;i<m;i++) - { - for(int j=0;j<n;j++) - { - values[c] = resj[j*(int)m+i]; - c++; - } - } - } - } - return true; -} - -/* - * Return either the sparsity structure of the Hessian of the Lagrangian, - * or the values of the Hessian of the Lagrangian for the given values for - * x,lambda,obj_factor. -*/ - -bool minconTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values) -{ - #ifdef DEBUG - sciprint("Code is in eval_h\n"); - #endif - double check; - if (values==NULL) - { - Index idx=0; - for (Index row = 0; row < numVars_; row++) - { - for (Index col = 0; col < numVars_; col++) - { - iRow[idx] = row; - jCol[idx] = col; - idx++; - } - } - } - else - { char name[20]="_gradhess"; - Number *resCh; - if (getHessFromScilab(n,m,name,x, &obj_factor, lambda, 7, 3,2,&resCh)) - { - return false; - } - Index index=0; - for (Index row=0;row < numVars_ ;++row) - { - for (Index col=0; col < numVars_; ++col) - { - values[index++]=resCh[numVars_*row+col]; - } - } - } - return true; -} - -void minconTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value) -{ - #ifdef DEBUG - sciprint("Code is in finalize_solution\n"); - sciprint("%d",status); - #endif - finalObjVal_ = obj_value; - status_ = status; - if(status==0 ||status== 3) - { - finalX_ = new double[n]; - for (Index i=0; i<numVars_; i++) - { - finalX_[i] = x[i]; - } - } -} - -const double * minconTMINLP::getX() -{ - return finalX_; -} - -double minconTMINLP::getObjVal() -{ - return finalObjVal_; -} - -int minconTMINLP::returnStatus() -{ - return status_; -} diff --git a/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp b/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp deleted file mode 100644 index a3212aa..0000000 --- a/newstructure/sci_gateway/cpp/sci_minuncTMINLP.cpp +++ /dev/null @@ -1,236 +0,0 @@ -// Copyright (C) 2015 - IIT Bombay - FOSSEE -// -// Author: Harpreet Singh, Pranav Deshpande and Akshay Miterani -// Organization: FOSSEE, IIT Bombay -// Email: toolbox@scilab.in -// This file must be used under the terms of the CeCILL. -// This source file is licensed as described in the file COPYING, which -// you should have received as part of this distribution. The terms -// are also available at -// http://www.cecill.info/licences/Licence_CeCILL_V2-en.txt - -#include "minuncTMINLP.hpp" -#include "sci_iofunc.hpp" - -extern "C" -{ -#include "call_scilab.h" -#include <api_scilab.h> -#include <Scierror.h> -#include <BOOL.h> -#include <localization.h> -#include <sciprint.h> -#include <string.h> -#include <assert.h> -} - -using namespace Ipopt; -using namespace Bonmin; - -minuncTMINLP::~minuncTMINLP() -{ - if(finalX_) delete[] finalX_; -} - -// Set the type of every variable - CONTINUOUS or INTEGER -bool minuncTMINLP::get_variables_types(Index n, VariableType* var_types) -{ - n = numVars_; - for(int i=0; i < n; i++) - var_types[i] = CONTINUOUS; - for(int i=0 ; i < intconSize_ ; ++i) - var_types[(int)(intcon_[i]-1)] = INTEGER; - return true; -} - -// The linearity of the variables - LINEAR or NON_LINEAR -bool minuncTMINLP::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types) -{ - /* - n = numVars_; - for(int i = 0; i < n; i++) - var_types[i] = Ipopt::TNLP::LINEAR; - */ - return true; -} - -// The linearity of the constraints - LINEAR or NON_LINEAR -bool minuncTMINLP::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types) -{ - /* m = numConstr_; - for(int i = 0; i < m; i++) - const_types[i] = Ipopt::TNLP::LINEAR; - */ - return true; -} - -//get NLP info such as number of variables,constraints,no.of elements in jacobian and hessian to allocate memory -bool minuncTMINLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style) -{ - n=numVars_; // Number of variables - m=0; // Number of constraints - nnz_jac_g = 0; // No. of elements in Jacobian of constraints - nnz_h_lag = n*(n+1)/2; // No. of elements in lower traingle of Hessian of the Lagrangian. - index_style=TNLP::C_STYLE; // Index style of matrices - return true; -} - -//get variable and constraint bound info -bool minuncTMINLP::get_bounds_info(Index n, Number* x_l, Number* x_u, Index m, Number* g_l, Number* g_u) -{ - unsigned int i; - for(i=0;i<n;i++) - { - x_l[i]=-1.0e19; - x_u[i]=1.0e19; - } - - g_l=NULL; - g_u=NULL; - return true; -} - -// return the value of the constraints: g(x) -bool minuncTMINLP::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g) -{ - // return the value of the constraints: g(x) - g=NULL; - return true; -} - -// return the structure or values of the jacobian -bool minuncTMINLP::eval_jac_g(Index n, const Number* x, bool new_x,Index m, Index nele_jac, Index* iRow, Index *jCol,Number* values) -{ - if (values == NULL) - { - // return the structure of the jacobian of the constraints - iRow=NULL; - jCol=NULL; - } - else - { - values=NULL; - } - - return true; -} - -//get value of objective function at vector x -bool minuncTMINLP::eval_f(Index n, const Number* x, bool new_x, Number& obj_value) -{ - char name[20]="_f"; - Number *obj; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&obj)) - { - return false; - } - obj_value = *obj; - return true; -} - -//get value of gradient of objective function at vector x. -bool minuncTMINLP::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f) -{ - char name[20]="_gradf"; - Number *resg; - if (getFunctionFromScilab(n,name,x, 7, 1, 2, &resg)) - { - return false; - } - - Index i; - for(i=0;i<numVars_;i++) - { - grad_f[i]=resg[i]; - } - return true; -} - -// This method sets initial values for required vectors . For now we are assuming 0 to all values. -bool minuncTMINLP::get_starting_point(Index n, bool init_x, Number* x,bool init_z, Number* z_L, Number* z_U,Index m, bool init_lambda,Number* lambda) -{ - assert(init_x == true); - assert(init_z == false); - assert(init_lambda == false); - if (init_x == true) - { //we need to set initial values for vector x - for (Index var=0;var<n;var++) - x[var]=varGuess_[var];//initialize with 0 or we can change. - } - - return true; -} - -/* - * Return either the sparsity structure of the Hessian of the Lagrangian, - * or the values of the Hessian of the Lagrangian for the given values for - * x,lambda,obj_factor. -*/ - -bool minuncTMINLP::eval_h(Index n, const Number* x, bool new_x,Number obj_factor, Index m, const Number* lambda,bool new_lambda, Index nele_hess, Index* iRow,Index* jCol, Number* values) -{ - double check; - if (values==NULL) - { - Index idx=0; - for (Index row = 0; row < numVars_; row++) - { - for (Index col = 0; col <= row; col++) - { - iRow[idx] = row; - jCol[idx] = col; - idx++; - } - } - } - - else - { - char name[20]="_gradhess"; - Number *resh; - if (getFunctionFromScilab(n,name,x, 7, 1,2,&resh)) - { - return false; - } - Index index=0; - for (Index row=0;row < numVars_ ;++row) - { - for (Index col=0; col <= row; ++col) - { - values[index++]=obj_factor*(resh[numVars_*row+col]); - } - } - return true; - } -} - - -void minuncTMINLP::finalize_solution(SolverReturn status,Index n, const Number* x, Number obj_value) -{ - finalObjVal_ = obj_value; - status_ = status; - if(status==0 ||status== 3) - { - finalX_ = new double[n]; - for (Index i=0; i<numVars_; i++) - { - finalX_[i] = x[i]; - } - } - -} - -const double * minuncTMINLP::getX() -{ - return finalX_; -} - -double minuncTMINLP::getObjVal() -{ - return finalObjVal_; -} - -int minuncTMINLP::returnStatus() -{ - return status_; -} diff --git a/newstructure/sci_gateway/loader_gateway.sce b/newstructure/sci_gateway/loader_gateway.sce deleted file mode 100644 index 6809d99..0000000 --- a/newstructure/sci_gateway/loader_gateway.sce +++ /dev/null @@ -1,24 +0,0 @@ -// This file is released under the 3-clause BSD license. See COPYING-BSD. -// Generated by builder_gateway.sce: Please, do not edit this file - -try - v = getversion("scilab"); -catch - v = [ 5 0 ]; // or older -end -if (v(1) <= 5) & (v(2) < 3) then - // new API in scilab 5.3 - error(gettext("Scilab 5.3 or more is required.")); -end - -sci_gateway_dir = get_absolute_file_path("loader_gateway.sce"); -current_dir = pwd(); - -chdir(sci_gateway_dir); -if ( isdir("cpp") ) then - chdir("cpp"); - exec("loader.sce"); -end - -chdir(current_dir); -clear sci_gateway_dir current_dir v; |