Added linux shared libraries and header files for int and ecos functions

1 files changed, 420 insertions, 0 deletions

diff --git a/thirdparty/linux/include/coin/BonTMINLP.hpp b/thirdparty/linux/include/coin/BonTMINLP.hpp
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+++ b/thirdparty/linux/include/coin/BonTMINLP.hpp
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+// (C) Copyright International Business Machines Corporation and
+// Carnegie Mellon University 2004, 2007
+//
+// All Rights Reserved.
+// This code is published under the Eclipse Public License.
+//
+// Authors :
+// Pierre Bonami, Carnegie Mellon University,
+// Carl D. Laird, Carnegie Mellon University,
+// Andreas Waechter, International Business Machines Corporation
+//
+// Date : 12/01/2004
+
+#ifndef __TMINLP_HPP__
+#define __TMINLP_HPP__
+
+#include "IpUtils.hpp"
+#include "IpReferenced.hpp"
+#include "IpException.hpp"
+#include "IpAlgTypes.hpp"
+#include "CoinPackedMatrix.hpp"
+#include "OsiCuts.hpp"
+#include "IpTNLP.hpp"
+#include "CoinError.hpp"
+#include "CoinHelperFunctions.hpp"
+
+namespace Bonmin
+{
+  DECLARE_STD_EXCEPTION(TMINLP_INVALID);
+  DECLARE_STD_EXCEPTION(TMINLP_INVALID_VARIABLE_BOUNDS);
+
+  /** Base class for all MINLPs that use a standard triplet matrix form
+   *  and dense vectors.
+   *  The class TMINLP2TNLP allows the caller to produce a viable TNLP
+   *  from the MINLP (by relaxing binary and/or integers, or by
+   *  fixing them), which can then be solved by Ipopt.
+   *
+   *  This interface presents the problem form:
+   *  \f[
+   *  \begin{array}{rl}
+   *     &min f(x)\\
+   *
+   *     \mbox{s.t.}&\\
+   *      &   g^L <= g(x) <= g^U\\
+   *
+   *       &   x^L <=  x   <= x^U\\
+   *   \end{array}
+   *  \f]
+   *  Where each x_i is either a continuous, binary, or integer variable.
+   *  If x_i is binary, the bounds [xL,xU] are assumed to be [0,1].
+   *  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 TMINLP : public Ipopt::ReferencedObject
+  {
+  public:
+    friend class TMINLP2TNLP;
+    /** Return statuses of algorithm.*/
+    enum SolverReturn{
+      SUCCESS,
+      INFEASIBLE,
+      CONTINUOUS_UNBOUNDED,
+      LIMIT_EXCEEDED,
+      USER_INTERRUPT,
+      MINLP_ERROR};
+    /** Class to store sos constraints for model */
+    struct SosInfo
+    {
+      /** Number of SOS constraints.*/
+      int num;
+      /** Type of sos. At present Only type '1' SOS are supported by Cbc*/
+      char * types;
+      /** priorities of sos constraints.*/
+      int * priorities;
+      
+      /** \name Sparse storage of the elements of the SOS constraints.*/
+      /** @{ */
+      /** Total number of non zeroes in SOS constraints.*/
+      int numNz;
+      /** For 0 <= i < nums, start[i] gives the indice of indices and weights arrays at which the description of constraints i begins..*/ 
+      int * starts;
+      /** indices of elements belonging to the SOS.*/
+      int * indices;
+      /** weights of the elements of the SOS.*/
+      double * weights;
+      /** @} */
+      /** default constructor. */
+      SosInfo();
+      /** Copy constructor.*/
+      SosInfo(const SosInfo & source);
+      
+
+      /** destructor*/
+      ~SosInfo()
+      {
+        gutsOfDestructor();
+      }
+
+
+      /** Reset information */
+      void gutsOfDestructor();
+
+    };
+
+    /** Stores branching priorities information. */
+    struct BranchingInfo
+    {
+      /**number of variables*/
+      int size;
+      /** User set priorities on variables. */
+      int * priorities;
+      /** User set preferered branching direction. */
+      int * branchingDirections;
+      /** User set up pseudo costs.*/
+      double * upPsCosts;
+      /** User set down pseudo costs.*/
+      double * downPsCosts;
+      BranchingInfo():
+      size(0),
+      priorities(NULL),
+      branchingDirections(NULL),
+      upPsCosts(NULL),
+      downPsCosts(NULL)
+      {}
+      BranchingInfo(const BranchingInfo &other)
+      {
+        gutsOfDestructor();
+        size = other.size;
+        priorities = CoinCopyOfArray(other.priorities, size);
+        branchingDirections = CoinCopyOfArray(other.branchingDirections, size);
+        upPsCosts = CoinCopyOfArray(other.upPsCosts, size);
+        downPsCosts = CoinCopyOfArray(other.downPsCosts, size);
+      }
+      void gutsOfDestructor()
+      {
+      if (priorities != NULL) delete [] priorities;
+      priorities = NULL;
+      if (branchingDirections != NULL) delete [] branchingDirections;  
+      branchingDirections = NULL;
+      if (upPsCosts != NULL) delete [] upPsCosts;
+      upPsCosts = NULL;
+      if (downPsCosts != NULL) delete [] downPsCosts;
+      downPsCosts = NULL;
+      }
+      ~BranchingInfo()
+      {
+	gutsOfDestructor();
+      }
+    };
+
+    /** Class to store perturbation radii for variables in the model */
+    class PerturbInfo
+    {
+    public:
+      /** default constructor. */
+      PerturbInfo() :
+	perturb_radius_(NULL)
+      {}
+
+      /** destructor*/
+      ~PerturbInfo()
+      {
+        delete [] perturb_radius_;
+      }
+
+      /** Method for setting the perturbation radii. */
+      void SetPerturbationArray(Ipopt::Index numvars, const double* perturb_radius);
+
+      /** Method for getting the array for the perturbation radii in
+       *  order to use the values. */
+      const double* GetPerturbationArray() const {
+	return perturb_radius_;
+      }
+
+    private:
+      /** Copy constructor.*/
+      PerturbInfo(const PerturbInfo & source);
+
+      /** Perturbation radii for all variables.  A negative value
+       *  means that the radius has not been given. If the pointer is
+       *  NULL, then no variables have been assigned a perturbation
+       *  radius. */
+      double* perturb_radius_;
+    };
+
+    /** Type of the variables.*/
+    enum VariableType
+    {
+      CONTINUOUS,
+      BINARY,
+      INTEGER
+    };
+
+    /**@name Constructors/Destructors */
+    //@{
+    TMINLP();
+
+    /** Default destructor */
+    virtual ~TMINLP();
+    //@}
+
+    /**@name methods to gather information about the MINLP */
+    //@{
+    /** overload this method to return the number of variables
+     *  and constraints, and the number of non-zeros in the jacobian and
+     *  the hessian. */
+    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)=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(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)
+    {
+      return false;
+    }
+
+
+    /** overload this method to provide the variables types. The var_types
+     *  array will be allocated with length n. */
+    virtual bool get_variables_types(Ipopt::Index n, VariableType* var_types)=0;
+
+    /** overload this method to provide the variables linearity.
+     * array should be allocated with length at least n.*/
+    virtual bool get_variables_linearity(Ipopt::Index n, 
+					   Ipopt::TNLP::LinearityType* var_types) = 0;
+
+    /** overload this method to provide the constraint linearity.
+     * array should be allocated with length at least m.*/
+    virtual bool get_constraints_linearity(Ipopt::Index m, 
+					   Ipopt::TNLP::LinearityType* const_types) = 0;
+
+    /** 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.
+     *  An exception will be thrown if x_l and x_u are not 0,1 for binary variables
+     */
+    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)=0;
+
+    /** overload this method to return the starting point. The bools
+     *  init_x and init_lambda are both inputs and outputs. As inputs,
+     *  they indicate whether or not the algorithm wants you to
+     *  initialize x and lambda respectively. If, for some reason, the
+     *  algorithm wants you to initialize these and you cannot, set
+     *  the respective bool to false.
+     */
+    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)=0;
+
+    /** overload this method to return the value of the objective function */
+    virtual bool eval_f(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::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(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::Number* grad_f)=0;
+
+    /** overload this method to return the vector of constraint values */
+    virtual bool eval_g(Ipopt::Index n, const Ipopt::Number* x, bool new_x,
+        Ipopt::Index m, Ipopt::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(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)=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 */
+    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)=0;
+    /** 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)
+    {
+      std::cerr << "Method eval_gi not overloaded from TMINLP\n";
+      throw -1;
+    }
+    /** 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)
+    {
+      std::cerr << "Method eval_grad_gi not overloaded from TMINLP\n";
+      throw -1;
+    }
+    //@}
+
+    /** @name Solution Methods */
+    //@{
+    /** This method is called when the algorithm is complete so the TNLP can store/write the solution */
+    virtual void finalize_solution(TMINLP::SolverReturn status,
+                                   Ipopt::Index n, const Ipopt::Number* x, Ipopt::Number obj_value) =0;
+    //@}
+    
+    virtual const BranchingInfo * branchingInfo() const = 0;
+
+    virtual const SosInfo * sosConstraints() const = 0;
+
+    virtual const PerturbInfo* perturbInfo() const
+    {
+      return NULL;
+    }
+
+    /** Say if has a specific function to compute upper bounds*/
+    virtual bool hasUpperBoundingObjective(){
+      return false;}
+    
+    /** overload this method to return the value of an alternative objective function for
+      upper bounding (to use it hasUpperBoundingObjective should return true).*/
+    virtual bool eval_upper_bound_f(Ipopt::Index n, const Ipopt::Number* x,
+                                    Ipopt::Number& obj_value){ return false; }
+
+   /** Used to mark constraints of the problem.*/
+   enum Convexity {
+     Convex/** Constraint is convex.*/,
+     NonConvex/** Constraint is non-convex.*/,
+     SimpleConcave/** Constraint is concave of the simple form y >= F(x).*/};
+
+   /** Structure for marked non-convex constraints. With possibility of
+       storing index of a constraint relaxing the non-convex constraint*/
+   struct MarkedNonConvex {
+      /** Default constructor gives "safe" values.*/
+	 MarkedNonConvex():
+	 cIdx(-1), cRelaxIdx(-1){}
+	 /** Index of the nonconvex constraint.*/
+      int cIdx;
+	 /** Index of constraint relaxing the nonconvex constraint.*/
+	 int cRelaxIdx;};
+   /** Structure which describes a constraints of the form
+       $f[ y \gt F(x) \f]
+	  with \f$ F(x) \f$ a concave function.*/
+   struct SimpleConcaveConstraint{
+      /** Default constructor gives "safe" values.*/
+	 SimpleConcaveConstraint():
+	   xIdx(-1), yIdx(-1), cIdx(-1){}
+      /** Index of the variable x.*/
+      int xIdx;
+      /** Index of the variable y.*/
+	 int yIdx;
+      /** Index of the constraint.*/
+	 int cIdx;};
+    /** Get accest to constraint convexities.*/
+    virtual bool get_constraint_convexities(int m, TMINLP::Convexity * constraints_convexities)const {
+      CoinFillN(constraints_convexities, m, TMINLP::Convex);
+      return true;}
+  /** Get dimension information on nonconvex constraints.*/
+  virtual bool get_number_nonconvex(int & number_non_conv, int & number_concave) const{
+    number_non_conv = 0;
+    number_concave = 0;
+    return true;} 
+  /** Get array describing the constraints marked nonconvex in the model.*/
+  virtual bool get_constraint_convexities(int number_non_conv, MarkedNonConvex * non_convs) const{
+    assert(number_non_conv == 0);
+    return true;}
+  /** Fill array containing indices of simple concave constraints.*/ 
+  virtual bool get_simple_concave_constraints(int number_concave, SimpleConcaveConstraint * simple_concave) const{
+    assert(number_concave == 0);
+    return true;}
+
+  /** Say if problem has a linear objective (for OA) */
+  virtual bool hasLinearObjective(){return false;}
+
+  /** Say if problem has general integer variables.*/
+  bool hasGeneralInteger();
+
+  /** Access array describing constraint to which perspectives should be applied.*/
+  virtual const int * get_const_xtra_id() const{
+    return NULL;
+  }
+  protected:
+    /** Copy constructor */
+    //@{
+    /** Copy Constructor */
+    TMINLP(const TMINLP&);
+
+    /** Overloaded Equals Operator */
+    void operator=(const TMINLP&);
+    //@}
+
+  private:
+  };
+
+} // namespace Ipopt
+
+#endif
+