From 4b64cf486f5c999fd8167758cae27839f3b50848 Mon Sep 17 00:00:00 2001
From: Harpreet
Date: Sat, 3 Sep 2016 00:34:27 +0530
Subject: Structure updated and intqpipopt files added

---
 .../linux/include/coin/CoinPresolveDual.hpp        | 85 ++++++++++++++++++++++
 1 file changed, 85 insertions(+)
 create mode 100644 newstructure/thirdparty/linux/include/coin/CoinPresolveDual.hpp

(limited to 'newstructure/thirdparty/linux/include/coin/CoinPresolveDual.hpp')

diff --git a/newstructure/thirdparty/linux/include/coin/CoinPresolveDual.hpp b/newstructure/thirdparty/linux/include/coin/CoinPresolveDual.hpp
new file mode 100644
index 0000000..b021ce0
--- /dev/null
+++ b/newstructure/thirdparty/linux/include/coin/CoinPresolveDual.hpp
@@ -0,0 +1,85 @@
+/* $Id: CoinPresolveDual.hpp 1510 2011-12-08 23:56:01Z lou $ */
+
+// Copyright (C) 2002, International Business Machines
+// Corporation and others.  All Rights Reserved.
+// This code is licensed under the terms of the Eclipse Public License (EPL).
+
+#ifndef CoinPresolveDual_H
+#define CoinPresolveDual_H
+
+/*! \class remove_dual_action
+    \brief Attempt to fix variables by bounding reduced costs
+
+  The reduced cost of x_j is d_j = c_j - y*a_j (1). Assume minimization,
+  so that at optimality d_j >= 0 for x_j nonbasic at lower bound, and
+  d_j <= 0 for x_j nonbasic at upper bound.
+ 
+  For a slack variable s_i, c_(n+i) = 0 and a_(n+i) is a unit vector, hence
+  d_(n+i) = -y_i. If s_i has a finite lower bound and no upper bound, we
+  must have y_i <= 0 at optimality. Similarly, if s_i has no lower bound and a
+  finite upper bound, we must have y_i >= 0.
+
+  For a singleton variable x_j, d_j = c_j - y_i*a_ij. Given x_j with a
+  single finite bound, we can bound d_j greater or less than 0 at
+  optimality, and that allows us to calculate an upper or lower bound on y_i
+  (depending on the bound on d_j and the sign of a_ij).
+
+  Now we have bounds on some subset of the y_i, and we can use these to
+  calculate upper and lower bounds on the d_j, using bound propagation on
+  (1). If we can manage to bound some d_j as strictly positive or strictly
+  negative, then at optimality the corresponding variable must be nonbasic
+  at its lower or upper bound, respectively. If the required bound is lacking,
+  the problem is unbounded.
+*/
+
+class remove_dual_action : public CoinPresolveAction {
+
+  public:
+
+  /// Destructor
+  ~remove_dual_action () ;
+
+  /// Name
+  inline const char *name () const { return ("remove_dual_action") ; }
+
+  /*! \brief Attempt to fix variables by bounding reduced costs
+
+    Always scans all variables. Propagates bounds on reduced costs until there's
+    no change or until some set of variables can be fixed.
+  */
+  static const CoinPresolveAction *presolve(CoinPresolveMatrix *prob,
+					    const CoinPresolveAction *next) ;
+
+  /*! \brief Postsolve
+
+    In addition to fixing variables (handled by make_fixed_action), we may
+    need use our own postsolve to restore constraint bounds.
+  */
+  void postsolve (CoinPostsolveMatrix *prob) const ;
+
+  private:
+
+  /// Postsolve (bound restore) instruction
+  struct action {
+    double rlo_ ;  ///< restored row lower bound
+    double rup_ ;  ///< restored row upper bound
+    int ndx_ ;     ///< row index
+  } ;
+
+  /// Constructor with postsolve actions.
+  remove_dual_action(int nactions, const action *actions,
+		     const CoinPresolveAction *next)
+    : CoinPresolveAction(next),
+      nactions_(nactions),
+      actions_(actions)
+  {}
+
+  /// Count of bound restore entries
+  const int nactions_ ;
+  /// Bound restore entries
+  const action *actions_ ;
+
+} ;
+#endif
+
+
-- 
cgit