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Diffstat (limited to 'build/Bonmin/include/coin/BonTMINLPLinObj.hpp')
| -rw-r--r-- | build/Bonmin/include/coin/BonTMINLPLinObj.hpp | 216 |
1 files changed, 0 insertions, 216 deletions
diff --git a/build/Bonmin/include/coin/BonTMINLPLinObj.hpp b/build/Bonmin/include/coin/BonTMINLPLinObj.hpp deleted file mode 100644 index 819bc57..0000000 --- a/build/Bonmin/include/coin/BonTMINLPLinObj.hpp +++ /dev/null @@ -1,216 +0,0 @@ -// (C) Copyright International Business Machines Corporation 2007 -// All Rights Reserved. -// -// Authors : -// Pierre Bonami, International Business Machines Corporation -// -// Date : 08/16/2007 - - -#ifndef TMINLPLinObj_H -#define TMINLPLinObj_H - -#include "BonTMINLP.hpp" - -namespace Bonmin { -/** From a TMINLP, this class adapts to another TMINLP where the original objective is transformed into a constraint - by adding an extra variable which is minimized. - - More precisely - \f[ - \begin{array}{l} - \min f(x)\\ - s.t\\ - g_l \leq g(x) \leq g_u\\ - x_l \leq x \leq u - \end{array} - \f] - is transformed ino - \begin{array}{l} - \min \eta\\ - s.t\\ - -\infty \leq f(x) - \eta \leq 0\\ - g_l \leq g(x) \leq g_u\\ - x_l \leq x \leq u - \end{array} - \f] - The objective is put as first constraint of the problem and the extra variable is the last one. - .*/ -class TMINLPLinObj: public Bonmin::TMINLP { - public: - /** Default constructor*/ - TMINLPLinObj(); - - /** destructor.*/ - virtual ~TMINLPLinObj(); - - /** set reference TMINLP */ - void setTminlp(Ipopt::SmartPtr<TMINLP> tminlp); - - /**@name methods to gather information about the MINLP */ - //@{ - /** Return the number of variables - * and constraints, and the number of non-zeros in the jacobian and - * the hessian. Call tminlp_ one but number of constraints and non-zeroes in the jacobian is stored internally.*/ - virtual bool get_nlp_info(Ipopt::Index& n, Ipopt::Index& m, Ipopt::Index& nnz_jac_g, - Ipopt::Index& nnz_h_lag, Ipopt::TNLP::IndexStyleEnum& index_style); - /** Return scaling parameters. If tminlp_ method returns true, translate - * constraint scaling (if asked). - */ - virtual bool get_scaling_parameters(Ipopt::Number& obj_scaling, - bool& use_x_scaling, Ipopt::Index n, - Ipopt::Number* x_scaling, - bool& use_g_scaling, Ipopt::Index m, - Ipopt::Number* g_scaling); - - - /** Get the variable type. Just call tminlp_'s method;. */ - virtual bool get_variables_types(Ipopt::Index n, VariableType* var_types){ - assert(IsValid(tminlp_)); - assert(n == n_); - var_types[n-1] = TMINLP::CONTINUOUS; - return tminlp_->get_variables_types(n - 1, var_types); - } - - /** Return the constraints linearity. Call tminlp_'s method and translate. - */ - virtual bool get_constraints_linearity(Ipopt::Index m, - Ipopt::TNLP::LinearityType* const_types); - - /** Return the information about the bound - * on the variables and constraints. Call tminlp_'s method and translate - * constraints bounds.*/ - virtual bool get_bounds_info(Ipopt::Index n, Ipopt::Number* x_l, Ipopt::Number* x_u, - Ipopt::Index m, Ipopt::Number* g_l, Ipopt::Number* g_u); - - /** Return the starting point. - Have to translate z_L and z_U. - */ - virtual bool get_starting_point(Ipopt::Index n, bool init_x, Ipopt::Number* x, - bool init_z, Ipopt::Number* z_L, Ipopt::Number* z_U, - Ipopt::Index m, bool init_lambda, - Ipopt::Number* lambda); - - /** Return the value of the objective function. - * Just call tminlp_ method. */ - virtual bool eval_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number& obj_value){ - assert(n == n_); - obj_value = x[n-1]; - return true;} - - /** Return the vector of the gradient of - * the objective w.r.t. x. Just call tminlp_ method. */ - virtual bool eval_grad_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number* grad_f){ - assert(IsValid(tminlp_)); - assert(n == n_); - n--; - for(int i = 0 ; i < n ; i++){ - grad_f[i] = 0;} - grad_f[n] = 1; - return true;} - - /** Return the vector of constraint values. - * Use tminlp_ functions and use mapping to get the needed values. */ - virtual bool eval_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index m, Ipopt::Number* g); - - /** Return the jacobian of the constraints. - * In first call nothing to change. In later just fix the values for the simple concaves - * and remove entries corresponding to nonConvex constraints. */ - virtual bool eval_jac_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index m, Ipopt::Index nele_jac, Ipopt::Index* iRow, - Ipopt::Index *jCol, Ipopt::Number* values); - - /** \brief Return the hessian of the lagrangian. - * Here we just put lambda in the correct format and call - * tminlp_'s function.*/ - virtual bool eval_h(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Number obj_factor, Ipopt::Index m, const Ipopt::Number* lambda, - bool new_lambda, Ipopt::Index nele_hess, - Ipopt::Index* iRow, Ipopt::Index* jCol, Ipopt::Number* values); - /** Compute the value of a single constraint. The constraint - * number is i (starting counting from 0. */ - virtual bool eval_gi(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index i, Ipopt::Number& gi); - /** Compute the structure or values of the gradient for one - * constraint. The constraint * number is i (starting counting - * from 0. Other things are like with eval_jac_g. */ - virtual bool eval_grad_gi(Ipopt::Index n, const Ipopt::Number* x, bool new_x, - Ipopt::Index i, Ipopt::Index& nele_grad_gi, Ipopt::Index* jCol, - Ipopt::Number* values); - //@} - - virtual bool get_variables_linearity(Ipopt::Index n, Ipopt::TNLP::LinearityType* c){ - assert(IsValid(tminlp_)); - assert(n == n_); - bool r_val = tminlp_->get_variables_linearity(n-1, c); - c[n - 1] = Ipopt::TNLP::LINEAR; - return r_val; - } - - - /** @name Solution Methods */ - //@{ - /** Use tminlp_ function.*/ - virtual void finalize_solution(TMINLP::SolverReturn status, - Ipopt::Index n, const Ipopt::Number* x, Ipopt::Number obj_value){ - return tminlp_->finalize_solution(status, n - 1, x, - obj_value); - } - //@} - - /** Use tminlp_ function.*/ - virtual const BranchingInfo * branchingInfo() const{ - return tminlp_->branchingInfo(); - } - - /** Use tminlp_ function. - \bug Has to translate sos information.*/ - virtual const SosInfo * sosConstraints() const{ - return tminlp_->sosConstraints(); - } - /** Use tminlp_ function.*/ - virtual const PerturbInfo* perturbInfo() const - { - return tminlp_->perturbInfo(); - } - - /** Use tminlp_ function.*/ - virtual bool hasUpperBoundingObjective(){ - assert(IsValid(tminlp_)); - return tminlp_->hasUpperBoundingObjective();} - - /** Use tminlp_ function.*/ - virtual bool eval_upper_bound_f(Ipopt::Index n, const Ipopt::Number* x, - Ipopt::Number& obj_value){ - assert(IsValid(tminlp_)); - return tminlp_->eval_upper_bound_f(n - 1, x, obj_value); } - - /** Say if problem has a linear objective (for OA) */ - virtual bool hasLinearObjective(){return true;} - /** return pointer to tminlp_.*/ - Ipopt::SmartPtr<TMINLP> tminlp(){return tminlp_;} - private: - /** Reset all data.*/ - void gutsOfDestructor(); - - /** Reference TMINLP which is to be relaxed.*/ - Ipopt::SmartPtr<TMINLP> tminlp_; - /** Ipopt::Number of constraints in the transformed MINLP.*/ - int m_; - /** Ipopt::Number of variables in the transformed MINLP.*/ - int n_; - /** number of non-zeroes in the jacobian of the transformed MINLP.*/ - int nnz_jac_; - /** offset for jacobian.*/ - int offset_; - -}; - - -}/* Ends Bonmin namepsace.*/ - -#endif - |