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Diffstat (limited to 'thirdparty/windows/include/coin/IpTNLP.hpp')
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diff --git a/thirdparty/windows/include/coin/IpTNLP.hpp b/thirdparty/windows/include/coin/IpTNLP.hpp new file mode 100644 index 0000000..c97ba88 --- /dev/null +++ b/thirdparty/windows/include/coin/IpTNLP.hpp @@ -0,0 +1,279 @@ +// Copyright (C) 2004, 2009 International Business Machines and others. +// All Rights Reserved. +// This code is published under the Common Public License. +// +// $Id: IpTNLP.hpp 1462 2009-06-02 04:17:13Z andreasw $ +// +// Authors: Carl Laird, Andreas Waechter IBM 2004-08-13 + +#ifndef __IPTNLP_HPP__ +#define __IPTNLP_HPP__ + +#include "IpUtils.hpp" +#include "IpReferenced.hpp" +#include "IpException.hpp" +#include "IpAlgTypes.hpp" +#include "IpReturnCodes.hpp" + +#include <map> + +namespace Ipopt +{ + // forward declarations + class IpoptData; + class IpoptCalculatedQuantities; + class IteratesVector; + + /** Base class for all NLP's that use standard triplet matrix form + * and dense vectors. This is the standard base class for all + * NLP's that use the standard triplet matrix form (as for Harwell + * routines) and dense vectors. The class TNLPAdapter then converts + * this interface to an interface that can be used directly by + * ipopt. + * + * This interface presents the problem form: + * + * min f(x) + * + * s.t. gL <= g(x) <= gU + * + * xL <= x <= xU + * + * In order to specify an equality constraint, set gL_i = gU_i = + * rhs. The value that indicates "infinity" for the bounds + * (i.e. the variable or constraint has no lower bound (-infinity) + * or upper bound (+infinity)) is set through the option + * nlp_lower_bound_inf and nlp_upper_bound_inf. To indicate that a + * variable has no upper or lower bound, set the bound to + * -ipopt_inf or +ipopt_inf respectively + */ + class TNLP : public ReferencedObject + { + public: + /** Type of the constraints*/ + enum LinearityType + { + LINEAR/** Constraint/Variable is linear.*/, + NON_LINEAR/**Constraint/Varaible is non-linear.*/ + }; + + /**@name Constructors/Destructors */ + //@{ + TNLP() + {} + + /** Default destructor */ + virtual ~TNLP() + {} + //@} + + DECLARE_STD_EXCEPTION(INVALID_TNLP); + + /**@name methods to gather information about the NLP */ + //@{ + /** overload this method to return the number of variables + * and constraints, and the number of non-zeros in the jacobian and + * the hessian. The index_style parameter lets you specify C or Fortran + * style indexing for the sparse matrix iRow and jCol parameters. + * C_STYLE is 0-based, and FORTRAN_STYLE is 1-based. + */ + enum IndexStyleEnum { C_STYLE=0, FORTRAN_STYLE=1 }; + virtual bool get_nlp_info(Index& n, Index& m, Index& nnz_jac_g, + Index& nnz_h_lag, IndexStyleEnum& index_style)=0; + + typedef std::map<std::string, std::vector<std::string> > StringMetaDataMapType; + typedef std::map<std::string, std::vector<Index> > IntegerMetaDataMapType; + typedef std::map<std::string, std::vector<Number> > NumericMetaDataMapType; + + /** overload this method to return any meta data for + * the variables and the constraints */ + virtual bool get_var_con_metadata(Index n, + StringMetaDataMapType& var_string_md, + IntegerMetaDataMapType& var_integer_md, + NumericMetaDataMapType& var_numeric_md, + Index m, + StringMetaDataMapType& con_string_md, + IntegerMetaDataMapType& con_integer_md, + NumericMetaDataMapType& con_numeric_md) + + { + return false; + } + + /** overload this method to return the information about the bound + * on the variables and constraints. The value that indicates + * that a bound does not exist is specified in the parameters + * nlp_lower_bound_inf and nlp_upper_bound_inf. By default, + * nlp_lower_bound_inf is -1e19 and nlp_upper_bound_inf is + * 1e19. (see TNLPAdapter) */ + virtual bool get_bounds_info(Index n, Number* x_l, Number* x_u, + Index m, Number* g_l, Number* g_u)=0; + + /** overload this method to return scaling parameters. This is + * only called if the options are set to retrieve user scaling. + * There, use_x_scaling (or use_g_scaling) should get set to true + * only if the variables (or constraints) are to be scaled. This + * method should return true only if the scaling parameters could + * be provided. + */ + virtual bool get_scaling_parameters(Number& obj_scaling, + bool& use_x_scaling, Index n, + Number* x_scaling, + bool& use_g_scaling, Index m, + Number* g_scaling) + { + return false; + } + + /** overload this method to return the variables linearity + * (TNLP::Linear or TNLP::NonLinear). The var_types + * array should be allocated with length at least n. (default implementation + * just return false and does not fill the array).*/ + virtual bool get_variables_linearity(Index n, LinearityType* var_types) + { + return false; + } + + /** overload this method to return the constraint linearity. + * array should be alocated with length at least n. (default implementation + * just return false and does not fill the array).*/ + virtual bool get_constraints_linearity(Index m, LinearityType* const_types) + { + return false; + } + + /** overload this method to return the starting point. The bool + * variables indicate whether the algorithm wants you to + * initialize x, z_L/z_u, and lambda, respectively. If, for some + * reason, the algorithm wants you to initialize these and you + * cannot, return false, which will cause Ipopt to stop. You + * will have to run Ipopt with different options then. + */ + 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)=0; + + /** overload this method to provide an Ipopt iterate (already in + * the form Ipopt requires it internally) for a warm start. + * Since this is only for expert users, a default dummy + * implementation is provided and returns false. */ + virtual bool get_warm_start_iterate(IteratesVector& warm_start_iterate) + { + return false; + } + + /** overload this method to return the value of the objective function */ + virtual bool eval_f(Index n, const Number* x, bool new_x, + Number& obj_value)=0; + + /** overload this method to return the vector of the gradient of + * the objective w.r.t. x */ + virtual bool eval_grad_f(Index n, const Number* x, bool new_x, + Number* grad_f)=0; + + /** overload this method to return the vector of constraint values */ + virtual bool eval_g(Index n, const Number* x, bool new_x, + Index m, Number* g)=0; + /** overload this method to return the jacobian of the + * constraints. The vectors iRow and jCol only need to be set + * once. The first call is used to set the structure only (iRow + * and jCol will be non-NULL, and values will be NULL) For + * subsequent calls, iRow and jCol will be 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)=0; + + /** overload this method to return the hessian of the + * lagrangian. The vectors iRow and jCol only need to be set once + * (during the first call). The first call is used to set the + * structure only (iRow and jCol will be non-NULL, and values + * will be NULL) For subsequent calls, iRow and jCol will be + * NULL. This matrix is symmetric - specify the lower diagonal + * only. A default implementation is provided, in case the user + * wants to se quasi-Newton approximations to estimate the second + * derivatives and doesn't not neet to implement this method. */ + 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) + { + return false; + } + //@} + + /** @name Solution Methods */ + //@{ + /** 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, const Number* z_L, const Number* z_U, + Index m, const Number* g, const Number* lambda, + Number obj_value, + const IpoptData* ip_data, + IpoptCalculatedQuantities* ip_cq)=0; + + /** Intermediate Callback method for the user. Providing dummy + * default implementation. For details see IntermediateCallBack + * in IpNLP.hpp. */ + virtual bool intermediate_callback(AlgorithmMode mode, + Index iter, Number obj_value, + Number inf_pr, Number inf_du, + Number mu, Number d_norm, + Number regularization_size, + Number alpha_du, Number alpha_pr, + Index ls_trials, + const IpoptData* ip_data, + IpoptCalculatedQuantities* ip_cq) + { + return true; + } + //@} + + /** @name Methods for quasi-Newton approximation. If the second + * derivatives are approximated by Ipopt, it is better to do this + * only in the space of nonlinear variables. The following + * methods are call by Ipopt if the quasi-Newton approximation is + * selected. If -1 is returned as number of nonlinear variables, + * Ipopt assumes that all variables are nonlinear. Otherwise, it + * calls get_list_of_nonlinear_variables with an array into which + * the indices of the nonlinear variables should be written - the + * array has the lengths num_nonlin_vars, which is identical with + * the return value of get_number_of_nonlinear_variables(). It + * is assumed that the indices are counted starting with 1 in the + * FORTRAN_STYLE, and 0 for the C_STYLE. */ + //@{ + virtual Index get_number_of_nonlinear_variables() + { + return -1; + } + + virtual bool get_list_of_nonlinear_variables(Index num_nonlin_vars, + Index* pos_nonlin_vars) + { + return false; + } + //@} + + private: + /**@name Default Compiler Generated Methods + * (Hidden to avoid implicit creation/calling). + * These methods are not implemented and + * we do not want the compiler to implement + * them for us, so we declare them private + * and do not define them. This ensures that + * they will not be implicitly created/called. */ + //@{ + /** Default Constructor */ + //TNLP(); + + /** Copy Constructor */ + TNLP(const TNLP&); + + /** Overloaded Equals Operator */ + void operator=(const TNLP&); + //@} + }; + +} // namespace Ipopt + +#endif |