cbcintlinprog added

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
deleted 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;