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+/* $Id: ClpSimplex.hpp 2114 2015-02-10 12:12:46Z forrest $ */
+// 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).
+/*
+   Authors
+
+   John Forrest
+
+ */
+#ifndef ClpSimplex_H
+#define ClpSimplex_H
+
+#include <iostream>
+#include <cfloat>
+#include "ClpModel.hpp"
+#include "ClpMatrixBase.hpp"
+#include "ClpSolve.hpp"
+#include "ClpConfig.h"
+class ClpDualRowPivot;
+class ClpPrimalColumnPivot;
+class ClpFactorization;
+class CoinIndexedVector;
+class ClpNonLinearCost;
+class ClpNodeStuff;
+class CoinStructuredModel;
+class OsiClpSolverInterface;
+class CoinWarmStartBasis;
+class ClpDisasterHandler;
+class ClpConstraint;
+/*
+  May want to use Clp defaults so that with ABC defined but not used
+  it behaves as Clp (and ABC used will be different than if not defined)
+ */
+#ifdef ABC_INHERIT
+#ifndef CLP_INHERIT_MODE
+#define CLP_INHERIT_MODE 1
+#endif
+#ifndef ABC_CLP_DEFAULTS
+#define ABC_CLP_DEFAULTS 0
+#endif
+#else
+#undef ABC_CLP_DEFAULTS
+#define ABC_CLP_DEFAULTS 1
+#endif
+#ifdef CLP_HAS_ABC
+#include "AbcCommon.hpp"
+class AbcTolerancesEtc;
+class AbcSimplex;
+#include "CoinAbcCommon.hpp"
+#endif
+/** This solves LPs using the simplex method
+
+    It inherits from ClpModel and all its arrays are created at
+    algorithm time. Originally I tried to work with model arrays
+    but for simplicity of coding I changed to single arrays with
+    structural variables then row variables.  Some coding is still
+    based on old style and needs cleaning up.
+
+    For a description of algorithms:
+
+    for dual see ClpSimplexDual.hpp and at top of ClpSimplexDual.cpp
+    for primal see ClpSimplexPrimal.hpp and at top of ClpSimplexPrimal.cpp
+
+    There is an algorithm data member.  + for primal variations
+    and - for dual variations
+
+*/
+
+class ClpSimplex : public ClpModel {
+     friend void ClpSimplexUnitTest(const std::string & mpsDir);
+
+public:
+     /** enums for status of various sorts.
+         First 4 match CoinWarmStartBasis,
+         isFixed means fixed at lower bound and out of basis
+     */
+     enum Status {
+          isFree = 0x00,
+          basic = 0x01,
+          atUpperBound = 0x02,
+          atLowerBound = 0x03,
+          superBasic = 0x04,
+          isFixed = 0x05
+     };
+     // For Dual
+     enum FakeBound {
+          noFake = 0x00,
+          lowerFake = 0x01,
+          upperFake = 0x02,
+          bothFake = 0x03
+     };
+
+     /**@name Constructors and destructor and copy */
+     //@{
+     /// Default constructor
+     ClpSimplex (bool emptyMessages = false  );
+
+     /** Copy constructor. May scale depending on mode
+         -1 leave mode as is
+         0 -off, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic(later)
+     */
+     ClpSimplex(const ClpSimplex & rhs, int scalingMode = -1);
+     /** Copy constructor from model. May scale depending on mode
+         -1 leave mode as is
+         0 -off, 1 equilibrium, 2 geometric, 3, auto, 4 dynamic(later)
+     */
+     ClpSimplex(const ClpModel & rhs, int scalingMode = -1);
+     /** Subproblem constructor.  A subset of whole model is created from the
+         row and column lists given.  The new order is given by list order and
+         duplicates are allowed.  Name and integer information can be dropped
+         Can optionally modify rhs to take into account variables NOT in list
+         in this case duplicates are not allowed (also see getbackSolution)
+     */
+     ClpSimplex (const ClpModel * wholeModel,
+                 int numberRows, const int * whichRows,
+                 int numberColumns, const int * whichColumns,
+                 bool dropNames = true, bool dropIntegers = true,
+                 bool fixOthers = false);
+     /** Subproblem constructor.  A subset of whole model is created from the
+         row and column lists given.  The new order is given by list order and
+         duplicates are allowed.  Name and integer information can be dropped
+         Can optionally modify rhs to take into account variables NOT in list
+         in this case duplicates are not allowed (also see getbackSolution)
+     */
+     ClpSimplex (const ClpSimplex * wholeModel,
+                 int numberRows, const int * whichRows,
+                 int numberColumns, const int * whichColumns,
+                 bool dropNames = true, bool dropIntegers = true,
+                 bool fixOthers = false);
+     /** This constructor modifies original ClpSimplex and stores
+         original stuff in created ClpSimplex.  It is only to be used in
+         conjunction with originalModel */
+     ClpSimplex (ClpSimplex * wholeModel,
+                 int numberColumns, const int * whichColumns);
+     /** This copies back stuff from miniModel and then deletes miniModel.
+         Only to be used with mini constructor */
+     void originalModel(ClpSimplex * miniModel);
+  inline int abcState() const
+  { return abcState_;}
+  inline void setAbcState(int state)
+  { abcState_=state;}
+#ifdef ABC_INHERIT
+  inline AbcSimplex * abcSimplex() const
+  { return abcSimplex_;}
+  inline void setAbcSimplex(AbcSimplex * simplex)
+  { abcSimplex_=simplex;}
+  /// Returns 0 if dual can be skipped
+  int doAbcDual();
+  /// Returns 0 if primal can be skipped
+  int doAbcPrimal(int ifValuesPass);
+#endif
+     /** Array persistence flag
+         If 0 then as now (delete/new)
+         1 then only do arrays if bigger needed
+         2 as 1 but give a bit extra if bigger needed
+     */
+     void setPersistenceFlag(int value);
+     /// Save a copy of model with certain state - normally without cuts
+     void makeBaseModel();
+     /// Switch off base model
+     void deleteBaseModel();
+     /// See if we have base model
+     inline ClpSimplex *  baseModel() const {
+          return baseModel_;
+     }
+     /** Reset to base model (just size and arrays needed)
+         If model NULL use internal copy
+     */
+     void setToBaseModel(ClpSimplex * model = NULL);
+     /// Assignment operator. This copies the data
+     ClpSimplex & operator=(const ClpSimplex & rhs);
+     /// Destructor
+     ~ClpSimplex (  );
+     // Ones below are just ClpModel with some changes
+     /** Loads a problem (the constraints on the
+           rows are given by lower and upper bounds). If a pointer is 0 then the
+           following values are the default:
+           <ul>
+             <li> <code>colub</code>: all columns have upper bound infinity
+             <li> <code>collb</code>: all columns have lower bound 0
+             <li> <code>rowub</code>: all rows have upper bound infinity
+             <li> <code>rowlb</code>: all rows have lower bound -infinity
+         <li> <code>obj</code>: all variables have 0 objective coefficient
+           </ul>
+       */
+     void loadProblem (  const ClpMatrixBase& matrix,
+                         const double* collb, const double* colub,
+                         const double* obj,
+                         const double* rowlb, const double* rowub,
+                         const double * rowObjective = NULL);
+     void loadProblem (  const CoinPackedMatrix& matrix,
+                         const double* collb, const double* colub,
+                         const double* obj,
+                         const double* rowlb, const double* rowub,
+                         const double * rowObjective = NULL);
+
+     /** Just like the other loadProblem() method except that the matrix is
+       given in a standard column major ordered format (without gaps). */
+     void loadProblem (  const int numcols, const int numrows,
+                         const CoinBigIndex* start, const int* index,
+                         const double* value,
+                         const double* collb, const double* colub,
+                         const double* obj,
+                         const double* rowlb, const double* rowub,
+                         const double * rowObjective = NULL);
+     /// This one is for after presolve to save memory
+     void loadProblem (  const int numcols, const int numrows,
+                         const CoinBigIndex* start, const int* index,
+                         const double* value, const int * length,
+                         const double* collb, const double* colub,
+                         const double* obj,
+                         const double* rowlb, const double* rowub,
+                         const double * rowObjective = NULL);
+     /** This loads a model from a coinModel object - returns number of errors.
+         If keepSolution true and size is same as current then
+         keeps current status and solution
+     */
+     int loadProblem (  CoinModel & modelObject, bool keepSolution = false);
+     /// Read an mps file from the given filename
+     int readMps(const char *filename,
+                 bool keepNames = false,
+                 bool ignoreErrors = false);
+     /// Read GMPL files from the given filenames
+     int readGMPL(const char *filename, const char * dataName,
+                  bool keepNames = false);
+     /// Read file in LP format from file with name filename.
+     /// See class CoinLpIO for description of this format.
+     int readLp(const char *filename, const double epsilon = 1e-5);
+     /** Borrow model.  This is so we dont have to copy large amounts
+         of data around.  It assumes a derived class wants to overwrite
+         an empty model with a real one - while it does an algorithm.
+         This is same as ClpModel one, but sets scaling on etc. */
+     void borrowModel(ClpModel & otherModel);
+     void borrowModel(ClpSimplex & otherModel);
+     /// Pass in Event handler (cloned and deleted at end)
+     void passInEventHandler(const ClpEventHandler * eventHandler);
+     /// Puts solution back into small model
+     void getbackSolution(const ClpSimplex & smallModel, const int * whichRow, const int * whichColumn);
+     /** Load nonlinear part of problem from AMPL info
+         Returns 0 if linear
+         1 if quadratic objective
+         2 if quadratic constraints
+         3 if nonlinear objective
+         4 if nonlinear constraints
+         -1 on failure
+     */
+     int loadNonLinear(void * info, int & numberConstraints,
+                       ClpConstraint ** & constraints);
+#ifdef ABC_INHERIT
+  /// Loads tolerances etc
+  void loadTolerancesEtc(const AbcTolerancesEtc & data);
+  /// Unloads tolerances etc
+  void unloadTolerancesEtc(AbcTolerancesEtc & data);
+#endif
+     //@}
+
+     /**@name Functions most useful to user */
+     //@{
+     /** General solve algorithm which can do presolve.
+         See  ClpSolve.hpp for options
+      */
+     int initialSolve(ClpSolve & options);
+     /// Default initial solve
+     int initialSolve();
+     /// Dual initial solve
+     int initialDualSolve();
+     /// Primal initial solve
+     int initialPrimalSolve();
+     /// Barrier initial solve
+     int initialBarrierSolve();
+     /// Barrier initial solve, not to be followed by crossover
+     int initialBarrierNoCrossSolve();
+     /** Dual algorithm - see ClpSimplexDual.hpp for method.
+         ifValuesPass==2 just does values pass and then stops.
+
+         startFinishOptions - bits
+         1 - do not delete work areas and factorization at end
+         2 - use old factorization if same number of rows
+         4 - skip as much initialization of work areas as possible
+             (based on whatsChanged in clpmodel.hpp) ** work in progress
+         maybe other bits later
+     */
+     int dual(int ifValuesPass = 0, int startFinishOptions = 0);
+     // If using Debug
+     int dualDebug(int ifValuesPass = 0, int startFinishOptions = 0);
+     /** Primal algorithm - see ClpSimplexPrimal.hpp for method.
+         ifValuesPass==2 just does values pass and then stops.
+
+         startFinishOptions - bits
+         1 - do not delete work areas and factorization at end
+         2 - use old factorization if same number of rows
+         4 - skip as much initialization of work areas as possible
+             (based on whatsChanged in clpmodel.hpp) ** work in progress
+         maybe other bits later
+     */
+     int primal(int ifValuesPass = 0, int startFinishOptions = 0);
+     /** Solves nonlinear problem using SLP - may be used as crash
+         for other algorithms when number of iterations small.
+         Also exits if all problematical variables are changing
+         less than deltaTolerance
+     */
+     int nonlinearSLP(int numberPasses, double deltaTolerance);
+     /** Solves problem with nonlinear constraints using SLP - may be used as crash
+         for other algorithms when number of iterations small.
+         Also exits if all problematical variables are changing
+         less than deltaTolerance
+     */
+     int nonlinearSLP(int numberConstraints, ClpConstraint ** constraints,
+                      int numberPasses, double deltaTolerance);
+     /** Solves using barrier (assumes you have good cholesky factor code).
+         Does crossover to simplex if asked*/
+     int barrier(bool crossover = true);
+     /** Solves non-linear using reduced gradient.  Phase = 0 get feasible,
+         =1 use solution */
+     int reducedGradient(int phase = 0);
+     /// Solve using structure of model and maybe in parallel
+     int solve(CoinStructuredModel * model);
+#ifdef ABC_INHERIT
+  /** solvetype 0 for dual, 1 for primal
+      startup 1 for values pass
+      interrupt whether to pass across interrupt handler
+      add 10 to return AbcSimplex 
+  */
+  AbcSimplex * dealWithAbc(int solveType,int startUp,bool interrupt=false);
+  //void dealWithAbc(int solveType,int startUp,bool interrupt=false);
+#endif
+     /** This loads a model from a CoinStructuredModel object - returns number of errors.
+         If originalOrder then keep to order stored in blocks,
+         otherwise first column/rows correspond to first block - etc.
+         If keepSolution true and size is same as current then
+         keeps current status and solution
+     */
+     int loadProblem (  CoinStructuredModel & modelObject,
+                        bool originalOrder = true, bool keepSolution = false);
+     /**
+        When scaling is on it is possible that the scaled problem
+        is feasible but the unscaled is not.  Clp returns a secondary
+        status code to that effect.  This option allows for a cleanup.
+        If you use it I would suggest 1.
+        This only affects actions when scaled optimal
+        0 - no action
+        1 - clean up using dual if primal infeasibility
+        2 - clean up using dual if dual infeasibility
+        3 - clean up using dual if primal or dual infeasibility
+        11,12,13 - as 1,2,3 but use primal
+
+        return code as dual/primal
+     */
+     int cleanup(int cleanupScaling);
+     /** Dual ranging.
+         This computes increase/decrease in cost for each given variable and corresponding
+         sequence numbers which would change basis.  Sequence numbers are 0..numberColumns
+         and numberColumns.. for artificials/slacks.
+         For non-basic variables the information is trivial to compute and the change in cost is just minus the
+         reduced cost and the sequence number will be that of the non-basic variables.
+         For basic variables a ratio test is between the reduced costs for non-basic variables
+         and the row of the tableau corresponding to the basic variable.
+         The increase/decrease value is always >= 0.0
+
+         Up to user to provide correct length arrays where each array is of length numberCheck.
+         which contains list of variables for which information is desired.  All other
+         arrays will be filled in by function.  If fifth entry in which is variable 7 then fifth entry in output arrays
+         will be information for variable 7.
+
+         If valueIncrease/Decrease not NULL (both must be NULL or both non NULL) then these are filled with
+         the value of variable if such a change in cost were made (the existing bounds are ignored)
+
+         Returns non-zero if infeasible unbounded etc
+     */
+     int dualRanging(int numberCheck, const int * which,
+                     double * costIncrease, int * sequenceIncrease,
+                     double * costDecrease, int * sequenceDecrease,
+                     double * valueIncrease = NULL, double * valueDecrease = NULL);
+     /** Primal ranging.
+         This computes increase/decrease in value for each given variable and corresponding
+         sequence numbers which would change basis.  Sequence numbers are 0..numberColumns
+         and numberColumns.. for artificials/slacks.
+         This should only be used for non-basic variabls as otherwise information is pretty useless
+         For basic variables the sequence number will be that of the basic variables.
+
+         Up to user to provide correct length arrays where each array is of length numberCheck.
+         which contains list of variables for which information is desired.  All other
+         arrays will be filled in by function.  If fifth entry in which is variable 7 then fifth entry in output arrays
+         will be information for variable 7.
+
+         Returns non-zero if infeasible unbounded etc
+     */
+     int primalRanging(int numberCheck, const int * which,
+                       double * valueIncrease, int * sequenceIncrease,
+                       double * valueDecrease, int * sequenceDecrease);
+     /**
+	Modifies coefficients etc and if necessary pivots in and out.
+	All at same status will be done (basis may go singular).
+	User can tell which others have been done (i.e. if status matches).
+	If called from outside will change status and return 0.
+	If called from event handler returns non-zero if user has to take action.
+	indices>=numberColumns are slacks (obviously no coefficients)
+	status array is (char) Status enum
+     */
+     int modifyCoefficientsAndPivot(int number,
+				 const int * which,
+				 const CoinBigIndex * start,
+				 const int * row,
+				 const double * newCoefficient,
+				 const unsigned char * newStatus=NULL,
+				 const double * newLower=NULL,
+				 const double * newUpper=NULL,
+				 const double * newObjective=NULL);
+     /** Take out duplicate rows (includes scaled rows and intersections).
+	 On exit whichRows has rows to delete - return code is number can be deleted 
+	 or -1 if would be infeasible.
+	 If tolerance is -1.0 use primalTolerance for equality rows and infeasibility
+	 If cleanUp not zero then spend more time trying to leave more stable row
+	 and make row bounds exact multiple of cleanUp if close enough
+     */
+     int outDuplicateRows(int numberLook,int * whichRows, bool noOverlaps=false, double tolerance=-1.0,
+			  double cleanUp=0.0);
+     /** Try simple crash like techniques to get closer to primal feasibility
+	 returns final sum of infeasibilities */
+     double moveTowardsPrimalFeasible();
+     /** Try simple crash like techniques to remove super basic slacks
+	 but only if > threshold */
+     void removeSuperBasicSlacks(int threshold=0);
+     /** Mini presolve (faster)
+	 Char arrays must be numberRows and numberColumns long
+	 on entry second part must be filled in as follows -
+	 0 - possible
+	 >0 - take out and do something (depending on value - TBD)
+	 -1 row/column can't vanish but can have entries removed/changed
+	 -2 don't touch at all
+	 on exit <=0 ones will be in presolved problem
+	 struct will be created and will be long enough
+	 (information on length etc in first entry)
+	 user must delete struct
+     */
+     ClpSimplex * miniPresolve(char * rowType, char * columnType,void ** info);
+     /// After mini presolve
+     void miniPostsolve(const ClpSimplex * presolvedModel,void * info);
+     /// mini presolve and solve
+     void miniSolve(char * rowType, char *columnType,int algorithm, int startUp);
+     /** Write the basis in MPS format to the specified file.
+         If writeValues true writes values of structurals
+         (and adds VALUES to end of NAME card)
+
+         Row and column names may be null.
+         formatType is
+         <ul>
+         <li> 0 - normal
+         <li> 1 - extra accuracy
+         <li> 2 - IEEE hex (later)
+         </ul>
+
+         Returns non-zero on I/O error
+     */
+     int writeBasis(const char *filename,
+                    bool writeValues = false,
+                    int formatType = 0) const;
+     /** Read a basis from the given filename,
+         returns -1 on file error, 0 if no values, 1 if values */
+     int readBasis(const char *filename);
+     /// Returns a basis (to be deleted by user)
+     CoinWarmStartBasis * getBasis() const;
+     /// Passes in factorization
+     void setFactorization( ClpFactorization & factorization);
+     // Swaps factorization
+     ClpFactorization * swapFactorization( ClpFactorization * factorization);
+     /// Copies in factorization to existing one
+     void copyFactorization( ClpFactorization & factorization);
+     /** Tightens primal bounds to make dual faster.  Unless
+         fixed or doTight>10, bounds are slightly looser than they could be.
+         This is to make dual go faster and is probably not needed
+         with a presolve.  Returns non-zero if problem infeasible.
+
+         Fudge for branch and bound - put bounds on columns of factor *
+         largest value (at continuous) - should improve stability
+         in branch and bound on infeasible branches (0.0 is off)
+     */
+     int tightenPrimalBounds(double factor = 0.0, int doTight = 0, bool tightIntegers = false);
+     /** Crash - at present just aimed at dual, returns
+         -2 if dual preferred and crash basis created
+         -1 if dual preferred and all slack basis preferred
+          0 if basis going in was not all slack
+          1 if primal preferred and all slack basis preferred
+          2 if primal preferred and crash basis created.
+
+          if gap between bounds <="gap" variables can be flipped
+          ( If pivot -1 then can be made super basic!)
+
+          If "pivot" is
+          -1 No pivoting - always primal
+          0 No pivoting (so will just be choice of algorithm)
+          1 Simple pivoting e.g. gub
+          2 Mini iterations
+     */
+     int crash(double gap, int pivot);
+     /// Sets row pivot choice algorithm in dual
+     void setDualRowPivotAlgorithm(ClpDualRowPivot & choice);
+     /// Sets column pivot choice algorithm in primal
+     void setPrimalColumnPivotAlgorithm(ClpPrimalColumnPivot & choice);
+     /// Create a hotstart point of the optimization process
+     void markHotStart(void * & saveStuff);
+     /// Optimize starting from the hotstart
+     void solveFromHotStart(void * saveStuff);
+     /// Delete the snapshot
+     void unmarkHotStart(void * saveStuff);
+     /** For strong branching.  On input lower and upper are new bounds
+         while on output they are change in objective function values
+         (>1.0e50 infeasible).
+         Return code is 0 if nothing interesting, -1 if infeasible both
+         ways and +1 if infeasible one way (check values to see which one(s))
+         Solutions are filled in as well - even down, odd up - also
+         status and number of iterations
+     */
+     int strongBranching(int numberVariables, const int * variables,
+                         double * newLower, double * newUpper,
+                         double ** outputSolution,
+                         int * outputStatus, int * outputIterations,
+                         bool stopOnFirstInfeasible = true,
+                         bool alwaysFinish = false,
+                         int startFinishOptions = 0);
+     /// Fathom - 1 if solution
+     int fathom(void * stuff);
+     /** Do up to N deep - returns
+         -1 - no solution nNodes_ valid nodes
+         >= if solution and that node gives solution
+         ClpNode array is 2**N long.  Values for N and
+         array are in stuff (nNodes_ also in stuff) */
+     int fathomMany(void * stuff);
+     /// Double checks OK
+     double doubleCheck();
+     /// Starts Fast dual2
+     int startFastDual2(ClpNodeStuff * stuff);
+     /// Like Fast dual
+     int fastDual2(ClpNodeStuff * stuff);
+     /// Stops Fast dual2
+     void stopFastDual2(ClpNodeStuff * stuff);
+     /** Deals with crunch aspects
+         mode 0 - in
+              1 - out with solution
+          2 - out without solution
+         returns small model or NULL
+     */
+     ClpSimplex * fastCrunch(ClpNodeStuff * stuff, int mode);
+     //@}
+
+     /**@name Needed for functionality of OsiSimplexInterface */
+     //@{
+     /** Pivot in a variable and out a variable.  Returns 0 if okay,
+         1 if inaccuracy forced re-factorization, -1 if would be singular.
+         Also updates primal/dual infeasibilities.
+         Assumes sequenceIn_ and pivotRow_ set and also directionIn and Out.
+     */
+     int pivot();
+
+     /** Pivot in a variable and choose an outgoing one.  Assumes primal
+         feasible - will not go through a bound.  Returns step length in theta
+         Returns ray in ray_ (or NULL if no pivot)
+         Return codes as before but -1 means no acceptable pivot
+     */
+     int primalPivotResult();
+
+     /** Pivot out a variable and choose an incoing one.  Assumes dual
+         feasible - will not go through a reduced cost.
+         Returns step length in theta
+         Return codes as before but -1 means no acceptable pivot
+     */
+     int dualPivotResultPart1();
+     /** Do actual pivot
+	 state is 0 if need tableau column, 1 if in rowArray_[1]
+     */
+  int pivotResultPart2(int algorithm,int state);
+
+     /** Common bits of coding for dual and primal.  Return 0 if okay,
+         1 if bad matrix, 2 if very bad factorization
+
+         startFinishOptions - bits
+         1 - do not delete work areas and factorization at end
+         2 - use old factorization if same number of rows
+         4 - skip as much initialization of work areas as possible
+             (based on whatsChanged in clpmodel.hpp) ** work in progress
+         maybe other bits later
+
+     */
+     int startup(int ifValuesPass, int startFinishOptions = 0);
+     void finish(int startFinishOptions = 0);
+
+     /** Factorizes and returns true if optimal.  Used by user */
+     bool statusOfProblem(bool initial = false);
+     /// If user left factorization frequency then compute
+     void defaultFactorizationFrequency();
+     /// Copy across enabled stuff from one solver to another
+     void copyEnabledStuff(const ClpSimplex * rhs);
+     //@}
+
+     /**@name most useful gets and sets */
+     //@{
+     /// If problem is primal feasible
+     inline bool primalFeasible() const {
+          return (numberPrimalInfeasibilities_ == 0);
+     }
+     /// If problem is dual feasible
+     inline bool dualFeasible() const {
+          return (numberDualInfeasibilities_ == 0);
+     }
+     /// factorization
+     inline ClpFactorization * factorization() const {
+          return factorization_;
+     }
+     /// Sparsity on or off
+     bool sparseFactorization() const;
+     void setSparseFactorization(bool value);
+     /// Factorization frequency
+     int factorizationFrequency() const;
+     void setFactorizationFrequency(int value);
+     /// Dual bound
+     inline double dualBound() const {
+          return dualBound_;
+     }
+     void setDualBound(double value);
+     /// Infeasibility cost
+     inline double infeasibilityCost() const {
+          return infeasibilityCost_;
+     }
+     void setInfeasibilityCost(double value);
+     /** Amount of print out:
+         0 - none
+         1 - just final
+         2 - just factorizations
+         3 - as 2 plus a bit more
+         4 - verbose
+         above that 8,16,32 etc just for selective debug
+     */
+     /** Perturbation:
+         50  - switch on perturbation
+         100 - auto perturb if takes too long (1.0e-6 largest nonzero)
+         101 - we are perturbed
+         102 - don't try perturbing again
+         default is 100
+         others are for playing
+     */
+     inline int perturbation() const {
+          return perturbation_;
+     }
+     void setPerturbation(int value);
+     /// Current (or last) algorithm
+     inline int algorithm() const {
+          return algorithm_;
+     }
+     /// Set algorithm
+     inline void setAlgorithm(int value) {
+          algorithm_ = value;
+     }
+     /// Return true if the objective limit test can be relied upon
+     bool isObjectiveLimitTestValid() const ;
+     /// Sum of dual infeasibilities
+     inline double sumDualInfeasibilities() const {
+          return sumDualInfeasibilities_;
+     }
+     inline void setSumDualInfeasibilities(double value) {
+          sumDualInfeasibilities_ = value;
+     }
+     /// Sum of relaxed dual infeasibilities
+     inline double sumOfRelaxedDualInfeasibilities() const {
+          return sumOfRelaxedDualInfeasibilities_;
+     }
+     inline void setSumOfRelaxedDualInfeasibilities(double value) {
+          sumOfRelaxedDualInfeasibilities_ = value;
+     }
+     /// Number of dual infeasibilities
+     inline int numberDualInfeasibilities() const {
+          return numberDualInfeasibilities_;
+     }
+     inline void setNumberDualInfeasibilities(int value) {
+          numberDualInfeasibilities_ = value;
+     }
+     /// Number of dual infeasibilities (without free)
+     inline int numberDualInfeasibilitiesWithoutFree() const {
+          return numberDualInfeasibilitiesWithoutFree_;
+     }
+     /// Sum of primal infeasibilities
+     inline double sumPrimalInfeasibilities() const {
+          return sumPrimalInfeasibilities_;
+     }
+     inline void setSumPrimalInfeasibilities(double value) {
+          sumPrimalInfeasibilities_ = value;
+     }
+     /// Sum of relaxed primal infeasibilities
+     inline double sumOfRelaxedPrimalInfeasibilities() const {
+          return sumOfRelaxedPrimalInfeasibilities_;
+     }
+     inline void setSumOfRelaxedPrimalInfeasibilities(double value) {
+          sumOfRelaxedPrimalInfeasibilities_ = value;
+     }
+     /// Number of primal infeasibilities
+     inline int numberPrimalInfeasibilities() const {
+          return numberPrimalInfeasibilities_;
+     }
+     inline void setNumberPrimalInfeasibilities(int value) {
+          numberPrimalInfeasibilities_ = value;
+     }
+     /** Save model to file, returns 0 if success.  This is designed for
+         use outside algorithms so does not save iterating arrays etc.
+     It does not save any messaging information.
+     Does not save scaling values.
+     It does not know about all types of virtual functions.
+     */
+     int saveModel(const char * fileName);
+     /** Restore model from file, returns 0 if success,
+         deletes current model */
+     int restoreModel(const char * fileName);
+
+     /** Just check solution (for external use) - sets sum of
+         infeasibilities etc.
+         If setToBounds 0 then primal column values not changed
+         and used to compute primal row activity values.  If 1 or 2
+         then status used - so all nonbasic variables set to
+         indicated bound and if any values changed (or ==2)  basic values re-computed.
+     */
+     void checkSolution(int setToBounds = 0);
+     /** Just check solution (for internal use) - sets sum of
+         infeasibilities etc. */
+     void checkSolutionInternal();
+     /// Check unscaled primal solution but allow for rounding error
+     void checkUnscaledSolution();
+     /// Useful row length arrays (0,1,2,3,4,5)
+     inline CoinIndexedVector * rowArray(int index) const {
+          return rowArray_[index];
+     }
+     /// Useful column length arrays (0,1,2,3,4,5)
+     inline CoinIndexedVector * columnArray(int index) const {
+          return columnArray_[index];
+     }
+     //@}
+
+     /******************** End of most useful part **************/
+     /**@name Functions less likely to be useful to casual user */
+     //@{
+     /** Given an existing factorization computes and checks
+         primal and dual solutions.  Uses input arrays for variables at
+         bounds.  Returns feasibility states */
+     int getSolution (  const double * rowActivities,
+                        const double * columnActivities);
+     /** Given an existing factorization computes and checks
+         primal and dual solutions.  Uses current problem arrays for
+         bounds.  Returns feasibility states */
+     int getSolution ();
+     /** Constructs a non linear cost from list of non-linearities (columns only)
+         First lower of each column is taken as real lower
+         Last lower is taken as real upper and cost ignored
+
+         Returns nonzero if bad data e.g. lowers not monotonic
+     */
+     int createPiecewiseLinearCosts(const int * starts,
+                                    const double * lower, const double * gradient);
+     /// dual row pivot choice
+     inline ClpDualRowPivot * dualRowPivot() const {
+          return dualRowPivot_;
+     }
+     /// primal column pivot choice
+     inline ClpPrimalColumnPivot * primalColumnPivot() const {
+          return primalColumnPivot_;
+     }
+     /// Returns true if model looks OK
+     inline bool goodAccuracy() const {
+          return (largestPrimalError_ < 1.0e-7 && largestDualError_ < 1.0e-7);
+     }
+     /** Return model - updates any scalars */
+     void returnModel(ClpSimplex & otherModel);
+     /** Factorizes using current basis.
+         solveType - 1 iterating, 0 initial, -1 external
+         If 10 added then in primal values pass
+         Return codes are as from ClpFactorization unless initial factorization
+         when total number of singularities is returned.
+         Special case is numberRows_+1 -> all slack basis.
+     */
+     int internalFactorize(int solveType);
+     /// Save data
+     ClpDataSave saveData() ;
+     /// Restore data
+     void restoreData(ClpDataSave saved);
+     /// Clean up status
+     void cleanStatus();
+     /// Factorizes using current basis. For external use
+     int factorize();
+     /** Computes duals from scratch. If givenDjs then
+         allows for nonzero basic djs */
+     void computeDuals(double * givenDjs);
+     /// Computes primals from scratch
+     void computePrimals (  const double * rowActivities,
+                            const double * columnActivities);
+     /** Adds multiple of a column into an array */
+     void add(double * array,
+              int column, double multiplier) const;
+     /**
+        Unpacks one column of the matrix into indexed array
+        Uses sequenceIn_
+        Also applies scaling if needed
+     */
+     void unpack(CoinIndexedVector * rowArray) const ;
+     /**
+        Unpacks one column of the matrix into indexed array
+        Slack if sequence>= numberColumns
+        Also applies scaling if needed
+     */
+     void unpack(CoinIndexedVector * rowArray, int sequence) const;
+     /**
+        Unpacks one column of the matrix into indexed array
+        ** as packed vector
+        Uses sequenceIn_
+        Also applies scaling if needed
+     */
+     void unpackPacked(CoinIndexedVector * rowArray) ;
+     /**
+        Unpacks one column of the matrix into indexed array
+        ** as packed vector
+        Slack if sequence>= numberColumns
+        Also applies scaling if needed
+     */
+     void unpackPacked(CoinIndexedVector * rowArray, int sequence);
+#ifndef CLP_USER_DRIVEN
+protected:
+#endif
+     /**
+         This does basis housekeeping and does values for in/out variables.
+         Can also decide to re-factorize
+     */
+     int housekeeping(double objectiveChange);
+     /** This sets largest infeasibility and most infeasible and sum
+         and number of infeasibilities (Primal) */
+     void checkPrimalSolution(const double * rowActivities = NULL,
+                              const double * columnActivies = NULL);
+     /** This sets largest infeasibility and most infeasible and sum
+         and number of infeasibilities (Dual) */
+     void checkDualSolution();
+     /** This sets sum and number of infeasibilities (Dual and Primal) */
+     void checkBothSolutions();
+     /**  If input negative scales objective so maximum <= -value
+          and returns scale factor used.  If positive unscales and also
+          redoes dual stuff
+     */
+     double scaleObjective(double value);
+     /// Solve using Dantzig-Wolfe decomposition and maybe in parallel
+    int solveDW(CoinStructuredModel * model, ClpSolve & options);
+     /// Solve using Benders decomposition and maybe in parallel
+     int solveBenders(CoinStructuredModel * model, ClpSolve & options);
+public:
+     /** For advanced use.  When doing iterative solves things can get
+         nasty so on values pass if incoming solution has largest
+         infeasibility < incomingInfeasibility throw out variables
+         from basis until largest infeasibility < allowedInfeasibility
+         or incoming largest infeasibility.
+         If allowedInfeasibility>= incomingInfeasibility this is
+         always possible altough you may end up with an all slack basis.
+
+         Defaults are 1.0,10.0
+     */
+     void setValuesPassAction(double incomingInfeasibility,
+                              double allowedInfeasibility);
+     /** Get a clean factorization - i.e. throw out singularities
+	 may do more later */
+     int cleanFactorization(int ifValuesPass);
+     //@}
+     /**@name most useful gets and sets */
+     //@{
+public:
+     /// Initial value for alpha accuracy calculation (-1.0 off)
+     inline double alphaAccuracy() const {
+          return alphaAccuracy_;
+     }
+     inline void setAlphaAccuracy(double value) {
+          alphaAccuracy_ = value;
+     }
+public:
+     /// Objective value
+     //inline double objectiveValue() const {
+     //return (objectiveValue_-bestPossibleImprovement_)*optimizationDirection_ - dblParam_[ClpObjOffset];
+     //}
+     /// Set disaster handler
+     inline void setDisasterHandler(ClpDisasterHandler * handler) {
+          disasterArea_ = handler;
+     }
+     /// Get disaster handler
+     inline ClpDisasterHandler * disasterHandler() const {
+          return disasterArea_;
+     }
+     /// Large bound value (for complementarity etc)
+     inline double largeValue() const {
+          return largeValue_;
+     }
+     void setLargeValue( double value) ;
+     /// Largest error on Ax-b
+     inline double largestPrimalError() const {
+          return largestPrimalError_;
+     }
+     /// Largest error on basic duals
+     inline double largestDualError() const {
+          return largestDualError_;
+     }
+     /// Largest error on Ax-b
+     inline void setLargestPrimalError(double value) {
+          largestPrimalError_ = value;
+     }
+     /// Largest error on basic duals
+     inline void setLargestDualError(double value) {
+          largestDualError_ = value;
+     }
+     /// Get zero tolerance
+     inline double zeroTolerance() const {
+          return zeroTolerance_;/*factorization_->zeroTolerance();*/
+     }
+     /// Set zero tolerance
+     inline void setZeroTolerance( double value) {
+          zeroTolerance_ = value;
+     }
+     /// Basic variables pivoting on which rows
+     inline int * pivotVariable() const {
+          return pivotVariable_;
+     }
+     /// If automatic scaling on
+     inline bool automaticScaling() const {
+          return automaticScale_ != 0;
+     }
+     inline void setAutomaticScaling(bool onOff) {
+          automaticScale_ = onOff ? 1 : 0;
+     }
+     /// Current dual tolerance
+     inline double currentDualTolerance() const {
+          return dualTolerance_;
+     }
+     inline void setCurrentDualTolerance(double value) {
+          dualTolerance_ = value;
+     }
+     /// Current primal tolerance
+     inline double currentPrimalTolerance() const {
+          return primalTolerance_;
+     }
+     inline void setCurrentPrimalTolerance(double value) {
+          primalTolerance_ = value;
+     }
+     /// How many iterative refinements to do
+     inline int numberRefinements() const {
+          return numberRefinements_;
+     }
+     void setNumberRefinements( int value) ;
+     /// Alpha (pivot element) for use by classes e.g. steepestedge
+     inline double alpha() const {
+          return alpha_;
+     }
+     inline void setAlpha(double value) {
+          alpha_ = value;
+     }
+     /// Reduced cost of last incoming for use by classes e.g. steepestedge
+     inline double dualIn() const {
+          return dualIn_;
+     }
+     /// Set reduced cost of last incoming to force error
+     inline void setDualIn(double value) {
+          dualIn_ = value;
+     }
+     /// Pivot Row for use by classes e.g. steepestedge
+     inline int pivotRow() const {
+          return pivotRow_;
+     }
+     inline void setPivotRow(int value) {
+          pivotRow_ = value;
+     }
+     /// value of incoming variable (in Dual)
+     double valueIncomingDual() const;
+     //@}
+
+#ifndef CLP_USER_DRIVEN
+protected:
+#endif
+     /**@name protected methods */
+     //@{
+     /** May change basis and then returns number changed.
+         Computation of solutions may be overriden by given pi and solution
+     */
+     int gutsOfSolution ( double * givenDuals,
+                          const double * givenPrimals,
+                          bool valuesPass = false);
+     /// Does most of deletion (0 = all, 1 = most, 2 most + factorization)
+     void gutsOfDelete(int type);
+     /// Does most of copying
+     void gutsOfCopy(const ClpSimplex & rhs);
+     /** puts in format I like (rowLower,rowUpper) also see StandardMatrix
+         1 bit does rows (now and columns), (2 bit does column bounds), 4 bit does objective(s).
+         8 bit does solution scaling in
+         16 bit does rowArray and columnArray indexed vectors
+         and makes row copy if wanted, also sets columnStart_ etc
+         Also creates scaling arrays if needed.  It does scaling if needed.
+         16 also moves solutions etc in to work arrays
+         On 16 returns false if problem "bad" i.e. matrix or bounds bad
+         If startFinishOptions is -1 then called by user in getSolution
+         so do arrays but keep pivotVariable_
+     */
+     bool createRim(int what, bool makeRowCopy = false, int startFinishOptions = 0);
+     /// Does rows and columns
+     void createRim1(bool initial);
+     /// Does objective
+     void createRim4(bool initial);
+     /// Does rows and columns and objective
+     void createRim5(bool initial);
+     /** releases above arrays and does solution scaling out.  May also
+         get rid of factorization data -
+         0 get rid of nothing, 1 get rid of arrays, 2 also factorization
+     */
+     void deleteRim(int getRidOfFactorizationData = 2);
+     /// Sanity check on input rim data (after scaling) - returns true if okay
+     bool sanityCheck();
+     //@}
+public:
+     /**@name public methods */
+     //@{
+     /** Return row or column sections - not as much needed as it
+         once was.  These just map into single arrays */
+     inline double * solutionRegion(int section) const {
+          if (!section) return rowActivityWork_;
+          else return columnActivityWork_;
+     }
+     inline double * djRegion(int section) const {
+          if (!section) return rowReducedCost_;
+          else return reducedCostWork_;
+     }
+     inline double * lowerRegion(int section) const {
+          if (!section) return rowLowerWork_;
+          else return columnLowerWork_;
+     }
+     inline double * upperRegion(int section) const {
+          if (!section) return rowUpperWork_;
+          else return columnUpperWork_;
+     }
+     inline double * costRegion(int section) const {
+          if (!section) return rowObjectiveWork_;
+          else return objectiveWork_;
+     }
+     /// Return region as single array
+     inline double * solutionRegion() const {
+          return solution_;
+     }
+     inline double * djRegion() const {
+          return dj_;
+     }
+     inline double * lowerRegion() const {
+          return lower_;
+     }
+     inline double * upperRegion() const {
+          return upper_;
+     }
+     inline double * costRegion() const {
+          return cost_;
+     }
+     inline Status getStatus(int sequence) const {
+          return static_cast<Status> (status_[sequence] & 7);
+     }
+     inline void setStatus(int sequence, Status newstatus) {
+          unsigned char & st_byte = status_[sequence];
+          st_byte = static_cast<unsigned char>(st_byte & ~7);
+          st_byte = static_cast<unsigned char>(st_byte | newstatus);
+     }
+     /// Start or reset using maximumRows_ and Columns_ - true if change
+     bool startPermanentArrays();
+     /** Normally the first factorization does sparse coding because
+         the factorization could be singular.  This allows initial dense
+         factorization when it is known to be safe
+     */
+     void setInitialDenseFactorization(bool onOff);
+     bool  initialDenseFactorization() const;
+     /** Return sequence In or Out */
+     inline int sequenceIn() const {
+          return sequenceIn_;
+     }
+     inline int sequenceOut() const {
+          return sequenceOut_;
+     }
+     /** Set sequenceIn or Out */
+     inline void  setSequenceIn(int sequence) {
+          sequenceIn_ = sequence;
+     }
+     inline void  setSequenceOut(int sequence) {
+          sequenceOut_ = sequence;
+     }
+     /** Return direction In or Out */
+     inline int directionIn() const {
+          return directionIn_;
+     }
+     inline int directionOut() const {
+          return directionOut_;
+     }
+     /** Set directionIn or Out */
+     inline void  setDirectionIn(int direction) {
+          directionIn_ = direction;
+     }
+     inline void  setDirectionOut(int direction) {
+          directionOut_ = direction;
+     }
+     /// Value of Out variable
+     inline double valueOut() const {
+          return valueOut_;
+     }
+     /// Set value of out variable
+     inline void setValueOut(double value) {
+          valueOut_ = value;
+     }
+     /// Dual value of Out variable
+     inline double dualOut() const {
+          return dualOut_;
+     }
+     /// Set dual value of out variable
+     inline void setDualOut(double value) {
+          dualOut_ = value;
+     }
+     /// Set lower of out variable
+     inline void setLowerOut(double value) {
+          lowerOut_ = value;
+     }
+     /// Set upper of out variable
+     inline void setUpperOut(double value) {
+          upperOut_ = value;
+     }
+     /// Set theta of out variable
+     inline void setTheta(double value) {
+          theta_ = value;
+     }
+     /// Returns 1 if sequence indicates column
+     inline int isColumn(int sequence) const {
+          return sequence < numberColumns_ ? 1 : 0;
+     }
+     /// Returns sequence number within section
+     inline int sequenceWithin(int sequence) const {
+          return sequence < numberColumns_ ? sequence : sequence - numberColumns_;
+     }
+     /// Return row or column values
+     inline double solution(int sequence) {
+          return solution_[sequence];
+     }
+     /// Return address of row or column values
+     inline double & solutionAddress(int sequence) {
+          return solution_[sequence];
+     }
+     inline double reducedCost(int sequence) {
+          return dj_[sequence];
+     }
+     inline double & reducedCostAddress(int sequence) {
+          return dj_[sequence];
+     }
+     inline double lower(int sequence) {
+          return lower_[sequence];
+     }
+     /// Return address of row or column lower bound
+     inline double & lowerAddress(int sequence) {
+          return lower_[sequence];
+     }
+     inline double upper(int sequence) {
+          return upper_[sequence];
+     }
+     /// Return address of row or column upper bound
+     inline double & upperAddress(int sequence) {
+          return upper_[sequence];
+     }
+     inline double cost(int sequence) {
+          return cost_[sequence];
+     }
+     /// Return address of row or column cost
+     inline double & costAddress(int sequence) {
+          return cost_[sequence];
+     }
+     /// Return original lower bound
+     inline double originalLower(int iSequence) const {
+          if (iSequence < numberColumns_) return columnLower_[iSequence];
+          else
+               return rowLower_[iSequence-numberColumns_];
+     }
+     /// Return original lower bound
+     inline double originalUpper(int iSequence) const {
+          if (iSequence < numberColumns_) return columnUpper_[iSequence];
+          else
+               return rowUpper_[iSequence-numberColumns_];
+     }
+     /// Theta (pivot change)
+     inline double theta() const {
+          return theta_;
+     }
+     /** Best possible improvement using djs (primal) or
+         obj change by flipping bounds to make dual feasible (dual) */
+     inline double bestPossibleImprovement() const {
+          return bestPossibleImprovement_;
+     }
+     /// Return pointer to details of costs
+     inline ClpNonLinearCost * nonLinearCost() const {
+          return nonLinearCost_;
+     }
+     /** Return more special options
+         1 bit - if presolve says infeasible in ClpSolve return
+         2 bit - if presolved problem infeasible return
+         4 bit - keep arrays like upper_ around
+         8 bit - if factorization kept can still declare optimal at once
+         16 bit - if checking replaceColumn accuracy before updating
+         32 bit - say optimal if primal feasible!
+         64 bit - give up easily in dual (and say infeasible)
+         128 bit - no objective, 0-1 and in B&B
+         256 bit - in primal from dual or vice versa
+         512 bit - alternative use of solveType_
+         1024 bit - don't do row copy of factorization
+	 2048 bit - perturb in complete fathoming
+	 4096 bit - try more for complete fathoming
+	 8192 bit - don't even think of using primal if user asks for dual (and vv)
+	 16384 bit - in initialSolve so be more flexible
+	 32768 bit - don't swap algorithms from dual if small infeasibility
+	 65536 bit - perturb in postsolve cleanup (even if < 10000 rows)
+	 131072 bit (*3) initial stateDualColumn
+	 524288 bit - stop when primal feasible
+     */
+     inline int moreSpecialOptions() const {
+          return moreSpecialOptions_;
+     }
+     /** Set more special options
+         1 bit - if presolve says infeasible in ClpSolve return
+         2 bit - if presolved problem infeasible return
+         4 bit - keep arrays like upper_ around
+         8 bit - no free or superBasic variables
+         16 bit - if checking replaceColumn accuracy before updating
+         32 bit - say optimal if primal feasible!
+         64 bit - give up easily in dual (and say infeasible)
+         128 bit - no objective, 0-1 and in B&B
+         256 bit - in primal from dual or vice versa
+         512 bit - alternative use of solveType_
+         1024 bit - don't do row copy of factorization
+	 2048 bit - perturb in complete fathoming
+	 4096 bit - try more for complete fathoming
+	 8192 bit - don't even think of using primal if user asks for dual (and vv)
+	 16384 bit - in initialSolve so be more flexible
+	 32768 bit - don't swap algorithms from dual if small infeasibility
+	 65536 bit - perturb in postsolve cleanup (even if < 10000 rows)
+	 131072 bit (*3) initial stateDualColumn
+	 524288 bit - stop when primal feasible
+	 1048576 bit - don't perturb even if long time
+	 2097152 bit - no primal in fastDual2 if feasible
+	 4194304 bit - tolerances have been changed by code
+	 8388608 bit - tolerances are dynamic (at first)
+     */
+     inline void setMoreSpecialOptions(int value) {
+          moreSpecialOptions_ = value;
+     }
+     //@}
+     /**@name status methods */
+     //@{
+     inline void setFakeBound(int sequence, FakeBound fakeBound) {
+          unsigned char & st_byte = status_[sequence];
+          st_byte = static_cast<unsigned char>(st_byte & ~24);
+          st_byte = static_cast<unsigned char>(st_byte | (fakeBound << 3));
+     }
+     inline FakeBound getFakeBound(int sequence) const {
+          return static_cast<FakeBound> ((status_[sequence] >> 3) & 3);
+     }
+     inline void setRowStatus(int sequence, Status newstatus) {
+          unsigned char & st_byte = status_[sequence+numberColumns_];
+          st_byte = static_cast<unsigned char>(st_byte & ~7);
+          st_byte = static_cast<unsigned char>(st_byte | newstatus);
+     }
+     inline Status getRowStatus(int sequence) const {
+          return static_cast<Status> (status_[sequence+numberColumns_] & 7);
+     }
+     inline void setColumnStatus(int sequence, Status newstatus) {
+          unsigned char & st_byte = status_[sequence];
+          st_byte = static_cast<unsigned char>(st_byte & ~7);
+          st_byte = static_cast<unsigned char>(st_byte | newstatus);
+     }
+     inline Status getColumnStatus(int sequence) const {
+          return static_cast<Status> (status_[sequence] & 7);
+     }
+     inline void setPivoted( int sequence) {
+          status_[sequence] = static_cast<unsigned char>(status_[sequence] | 32);
+     }
+     inline void clearPivoted( int sequence) {
+          status_[sequence] = static_cast<unsigned char>(status_[sequence] & ~32);
+     }
+     inline bool pivoted(int sequence) const {
+          return (((status_[sequence] >> 5) & 1) != 0);
+     }
+     /// To flag a variable (not inline to allow for column generation)
+     void setFlagged( int sequence);
+     inline void clearFlagged( int sequence) {
+          status_[sequence] = static_cast<unsigned char>(status_[sequence] & ~64);
+     }
+     inline bool flagged(int sequence) const {
+          return ((status_[sequence] & 64) != 0);
+     }
+     /// To say row active in primal pivot row choice
+     inline void setActive( int iRow) {
+          status_[iRow] = static_cast<unsigned char>(status_[iRow] | 128);
+     }
+     inline void clearActive( int iRow) {
+          status_[iRow] = static_cast<unsigned char>(status_[iRow] & ~128);
+     }
+     inline bool active(int iRow) const {
+          return ((status_[iRow] & 128) != 0);
+     }
+     /// To say perturbed 
+     inline void setPerturbed( int iSequence) {
+          status_[iSequence] = static_cast<unsigned char>(status_[iSequence] | 128);
+     }
+     inline void clearPerturbed( int iSequence) {
+          status_[iSequence] = static_cast<unsigned char>(status_[iSequence] & ~128);
+     }
+     inline bool perturbed(int iSequence) const {
+          return ((status_[iSequence] & 128) != 0);
+     }
+     /** Set up status array (can be used by OsiClp).
+         Also can be used to set up all slack basis */
+     void createStatus() ;
+     /** Sets up all slack basis and resets solution to
+         as it was after initial load or readMps */
+     void allSlackBasis(bool resetSolution = false);
+
+     /// So we know when to be cautious
+     inline int lastBadIteration() const {
+          return lastBadIteration_;
+     }
+     /// Set so we know when to be cautious
+     inline void setLastBadIteration(int value) {
+          lastBadIteration_=value;
+     }
+     /// Progress flag - at present 0 bit says artificials out
+     inline int progressFlag() const {
+          return (progressFlag_ & 3);
+     }
+     /// For dealing with all issues of cycling etc
+     inline ClpSimplexProgress * progress()
+     { return &progress_;}
+     /// Force re-factorization early value
+     inline int forceFactorization() const {
+          return forceFactorization_ ;
+     }
+     /// Force re-factorization early
+     inline void forceFactorization(int value) {
+          forceFactorization_ = value;
+     }
+     /// Raw objective value (so always minimize in primal)
+     inline double rawObjectiveValue() const {
+          return objectiveValue_;
+     }
+     /// Compute objective value from solution and put in objectiveValue_
+     void computeObjectiveValue(bool useWorkingSolution = false);
+     /// Compute minimization objective value from internal solution without perturbation
+     double computeInternalObjectiveValue();
+     /** Infeasibility/unbounded ray (NULL returned if none/wrong)
+         Up to user to use delete [] on these arrays.  */
+     double * infeasibilityRay(bool fullRay=false) const;
+     /** Number of extra rows.  These are ones which will be dynamically created
+         each iteration.  This is for GUB but may have other uses.
+     */
+     inline int numberExtraRows() const {
+          return numberExtraRows_;
+     }
+     /** Maximum number of basic variables - can be more than number of rows if GUB
+     */
+     inline int maximumBasic() const {
+          return maximumBasic_;
+     }
+     /// Iteration when we entered dual or primal
+     inline int baseIteration() const {
+          return baseIteration_;
+     }
+     /// Create C++ lines to get to current state
+     void generateCpp( FILE * fp, bool defaultFactor = false);
+     /// Gets clean and emptyish factorization
+     ClpFactorization * getEmptyFactorization();
+     /// May delete or may make clean and emptyish factorization
+     void setEmptyFactorization();
+     /// Move status and solution across
+     void moveInfo(const ClpSimplex & rhs, bool justStatus = false);
+     //@}
+
+     ///@name Basis handling
+     // These are only to be used using startFinishOptions (ClpSimplexDual, ClpSimplexPrimal)
+     // *** At present only without scaling
+     // *** Slacks havve -1.0 element (so == row activity) - take care
+     ///Get a row of the tableau (slack part in slack if not NULL)
+     void getBInvARow(int row, double* z, double * slack = NULL);
+
+     ///Get a row of the basis inverse
+     void getBInvRow(int row, double* z);
+
+     ///Get a column of the tableau
+     void getBInvACol(int col, double* vec);
+
+     ///Get a column of the basis inverse
+     void getBInvCol(int col, double* vec);
+
+     /** Get basic indices (order of indices corresponds to the
+         order of elements in a vector retured by getBInvACol() and
+         getBInvCol()).
+     */
+     void getBasics(int* index);
+
+     //@}
+     //-------------------------------------------------------------------------
+     /**@name Changing bounds on variables and constraints */
+     //@{
+     /** Set an objective function coefficient */
+     void setObjectiveCoefficient( int elementIndex, double elementValue );
+     /** Set an objective function coefficient */
+     inline void setObjCoeff( int elementIndex, double elementValue ) {
+          setObjectiveCoefficient( elementIndex, elementValue);
+     }
+
+     /** Set a single column lower bound<br>
+         Use -DBL_MAX for -infinity. */
+     void setColumnLower( int elementIndex, double elementValue );
+
+     /** Set a single column upper bound<br>
+         Use DBL_MAX for infinity. */
+     void setColumnUpper( int elementIndex, double elementValue );
+
+     /** Set a single column lower and upper bound */
+     void setColumnBounds( int elementIndex,
+                           double lower, double upper );
+
+     /** Set the bounds on a number of columns simultaneously<br>
+         The default implementation just invokes setColLower() and
+         setColUpper() over and over again.
+         @param indexFirst,indexLast pointers to the beginning and after the
+            end of the array of the indices of the variables whose
+        <em>either</em> bound changes
+         @param boundList the new lower/upper bound pairs for the variables
+     */
+     void setColumnSetBounds(const int* indexFirst,
+                             const int* indexLast,
+                             const double* boundList);
+
+     /** Set a single column lower bound<br>
+         Use -DBL_MAX for -infinity. */
+     inline void setColLower( int elementIndex, double elementValue ) {
+          setColumnLower(elementIndex, elementValue);
+     }
+     /** Set a single column upper bound<br>
+         Use DBL_MAX for infinity. */
+     inline void setColUpper( int elementIndex, double elementValue ) {
+          setColumnUpper(elementIndex, elementValue);
+     }
+
+     /** Set a single column lower and upper bound */
+     inline void setColBounds( int elementIndex,
+                               double newlower, double newupper ) {
+          setColumnBounds(elementIndex, newlower, newupper);
+     }
+
+     /** Set the bounds on a number of columns simultaneously<br>
+         @param indexFirst,indexLast pointers to the beginning and after the
+            end of the array of the indices of the variables whose
+        <em>either</em> bound changes
+         @param boundList the new lower/upper bound pairs for the variables
+     */
+     inline void setColSetBounds(const int* indexFirst,
+                                 const int* indexLast,
+                                 const double* boundList) {
+          setColumnSetBounds(indexFirst, indexLast, boundList);
+     }
+
+     /** Set a single row lower bound<br>
+         Use -DBL_MAX for -infinity. */
+     void setRowLower( int elementIndex, double elementValue );
+
+     /** Set a single row upper bound<br>
+         Use DBL_MAX for infinity. */
+     void setRowUpper( int elementIndex, double elementValue ) ;
+
+     /** Set a single row lower and upper bound */
+     void setRowBounds( int elementIndex,
+                        double lower, double upper ) ;
+
+     /** Set the bounds on a number of rows simultaneously<br>
+         @param indexFirst,indexLast pointers to the beginning and after the
+            end of the array of the indices of the constraints whose
+        <em>either</em> bound changes
+         @param boundList the new lower/upper bound pairs for the constraints
+     */
+     void setRowSetBounds(const int* indexFirst,
+                          const int* indexLast,
+                          const double* boundList);
+     /// Resizes rim part of model
+     void resize (int newNumberRows, int newNumberColumns);
+
+     //@}
+
+////////////////// data //////////////////
+protected:
+
+     /**@name data.  Many arrays have a row part and a column part.
+      There is a single array with both - columns then rows and
+      then normally two arrays pointing to rows and columns.  The
+      single array is the owner of memory
+     */
+     //@{
+     /** Best possible improvement using djs (primal) or
+         obj change by flipping bounds to make dual feasible (dual) */
+     double bestPossibleImprovement_;
+     /// Zero tolerance
+     double zeroTolerance_;
+     /// Sequence of worst (-1 if feasible)
+     int columnPrimalSequence_;
+     /// Sequence of worst (-1 if feasible)
+     int rowPrimalSequence_;
+     /// "Best" objective value
+     double bestObjectiveValue_;
+     /// More special options - see set for details
+     int moreSpecialOptions_;
+     /// Iteration when we entered dual or primal
+     int baseIteration_;
+     /// Primal tolerance needed to make dual feasible (<largeTolerance)
+     double primalToleranceToGetOptimal_;
+     /// Large bound value (for complementarity etc)
+     double largeValue_;
+     /// Largest error on Ax-b
+     double largestPrimalError_;
+     /// Largest error on basic duals
+     double largestDualError_;
+     /// For computing whether to re-factorize
+     double alphaAccuracy_;
+     /// Dual bound
+     double dualBound_;
+     /// Alpha (pivot element)
+     double alpha_;
+     /// Theta (pivot change)
+     double theta_;
+     /// Lower Bound on In variable
+     double lowerIn_;
+     /// Value of In variable
+     double valueIn_;
+     /// Upper Bound on In variable
+     double upperIn_;
+     /// Reduced cost of In variable
+     double dualIn_;
+     /// Lower Bound on Out variable
+     double lowerOut_;
+     /// Value of Out variable
+     double valueOut_;
+     /// Upper Bound on Out variable
+     double upperOut_;
+     /// Infeasibility (dual) or ? (primal) of Out variable
+     double dualOut_;
+     /// Current dual tolerance for algorithm
+     double dualTolerance_;
+     /// Current primal tolerance for algorithm
+     double primalTolerance_;
+     /// Sum of dual infeasibilities
+     double sumDualInfeasibilities_;
+     /// Sum of primal infeasibilities
+     double sumPrimalInfeasibilities_;
+     /// Weight assigned to being infeasible in primal
+     double infeasibilityCost_;
+     /// Sum of Dual infeasibilities using tolerance based on error in duals
+     double sumOfRelaxedDualInfeasibilities_;
+     /// Sum of Primal infeasibilities using tolerance based on error in primals
+     double sumOfRelaxedPrimalInfeasibilities_;
+     /// Acceptable pivot value just after factorization
+     double acceptablePivot_;
+     /// Minimum primal tolerance
+     double minimumPrimalTolerance_;
+     /// Last few infeasibilities
+#define CLP_INFEAS_SAVE 5
+     double averageInfeasibility_[CLP_INFEAS_SAVE];
+     /// Working copy of lower bounds (Owner of arrays below)
+     double * lower_;
+     /// Row lower bounds - working copy
+     double * rowLowerWork_;
+     /// Column lower bounds - working copy
+     double * columnLowerWork_;
+     /// Working copy of upper bounds (Owner of arrays below)
+     double * upper_;
+     /// Row upper bounds - working copy
+     double * rowUpperWork_;
+     /// Column upper bounds - working copy
+     double * columnUpperWork_;
+     /// Working copy of objective (Owner of arrays below)
+     double * cost_;
+     /// Row objective - working copy
+     double * rowObjectiveWork_;
+     /// Column objective - working copy
+     double * objectiveWork_;
+     /// Useful row length arrays
+     CoinIndexedVector * rowArray_[6];
+     /// Useful column length arrays
+     CoinIndexedVector * columnArray_[6];
+     /// Sequence of In variable
+     int sequenceIn_;
+     /// Direction of In, 1 going up, -1 going down, 0 not a clude
+     int directionIn_;
+     /// Sequence of Out variable
+     int sequenceOut_;
+     /// Direction of Out, 1 to upper bound, -1 to lower bound, 0 - superbasic
+     int directionOut_;
+     /// Pivot Row
+     int pivotRow_;
+     /// Last good iteration (immediately after a re-factorization)
+     int lastGoodIteration_;
+     /// Working copy of reduced costs (Owner of arrays below)
+     double * dj_;
+     /// Reduced costs of slacks not same as duals (or - duals)
+     double * rowReducedCost_;
+     /// Possible scaled reduced costs
+     double * reducedCostWork_;
+     /// Working copy of primal solution (Owner of arrays below)
+     double * solution_;
+     /// Row activities - working copy
+     double * rowActivityWork_;
+     /// Column activities - working copy
+     double * columnActivityWork_;
+     /// Number of dual infeasibilities
+     int numberDualInfeasibilities_;
+     /// Number of dual infeasibilities (without free)
+     int numberDualInfeasibilitiesWithoutFree_;
+     /// Number of primal infeasibilities
+     int numberPrimalInfeasibilities_;
+     /// How many iterative refinements to do
+     int numberRefinements_;
+     /// dual row pivot choice
+     ClpDualRowPivot * dualRowPivot_;
+     /// primal column pivot choice
+     ClpPrimalColumnPivot * primalColumnPivot_;
+     /// Basic variables pivoting on which rows
+     int * pivotVariable_;
+     /// factorization
+     ClpFactorization * factorization_;
+     /// Saved version of solution
+     double * savedSolution_;
+     /// Number of times code has tentatively thought optimal
+     int numberTimesOptimal_;
+     /// Disaster handler
+     ClpDisasterHandler * disasterArea_;
+     /// If change has been made (first attempt at stopping looping)
+     int changeMade_;
+     /// Algorithm >0 == Primal, <0 == Dual
+     int algorithm_;
+     /** Now for some reliability aids
+         This forces re-factorization early */
+     int forceFactorization_;
+     /** Perturbation:
+         -50 to +50 - perturb by this power of ten (-6 sounds good)
+         100 - auto perturb if takes too long (1.0e-6 largest nonzero)
+         101 - we are perturbed
+         102 - don't try perturbing again
+         default is 100
+     */
+     int perturbation_;
+     /// Saved status regions
+     unsigned char * saveStatus_;
+     /** Very wasteful way of dealing with infeasibilities in primal.
+         However it will allow non-linearities and use of dual
+         analysis.  If it doesn't work it can easily be replaced.
+     */
+     ClpNonLinearCost * nonLinearCost_;
+     /// So we know when to be cautious
+     int lastBadIteration_;
+     /// So we know when to open up again
+     int lastFlaggedIteration_;
+     /// Can be used for count of fake bounds (dual) or fake costs (primal)
+     int numberFake_;
+     /// Can be used for count of changed costs (dual) or changed bounds (primal)
+     int numberChanged_;
+     /// Progress flag - at present 0 bit says artificials out, 1 free in
+     int progressFlag_;
+     /// First free/super-basic variable (-1 if none)
+     int firstFree_;
+     /** Number of extra rows.  These are ones which will be dynamically created
+         each iteration.  This is for GUB but may have other uses.
+     */
+     int numberExtraRows_;
+     /** Maximum number of basic variables - can be more than number of rows if GUB
+     */
+     int maximumBasic_;
+     /// If may skip final factorize then allow up to this pivots (default 20)
+     int dontFactorizePivots_;
+     /** For advanced use.  When doing iterative solves things can get
+         nasty so on values pass if incoming solution has largest
+         infeasibility < incomingInfeasibility throw out variables
+         from basis until largest infeasibility < allowedInfeasibility.
+         if allowedInfeasibility>= incomingInfeasibility this is
+         always possible altough you may end up with an all slack basis.
+
+         Defaults are 1.0,10.0
+     */
+     double incomingInfeasibility_;
+     double allowedInfeasibility_;
+     /// Automatic scaling of objective and rhs and bounds
+     int automaticScale_;
+     /// Maximum perturbation array size (take out when code rewritten)
+     int maximumPerturbationSize_;
+     /// Perturbation array (maximumPerturbationSize_)
+     double * perturbationArray_;
+     /// A copy of model with certain state - normally without cuts
+     ClpSimplex * baseModel_;
+     /// For dealing with all issues of cycling etc
+     ClpSimplexProgress progress_;
+#ifdef ABC_INHERIT
+  AbcSimplex * abcSimplex_;
+#define CLP_ABC_WANTED 1
+#define CLP_ABC_WANTED_PARALLEL 2
+#define CLP_ABC_FULL_DONE 8
+  // bits 256,512,1024 for crash
+#endif
+#define CLP_ABC_BEEN_FEASIBLE 65536
+  int abcState_;
+  /// Number of degenerate pivots since last perturbed
+  int numberDegeneratePivots_;
+public:
+     /// Spare int array for passing information [0]!=0 switches on
+     mutable int spareIntArray_[4];
+     /// Spare double array for passing information [0]!=0 switches on
+     mutable double spareDoubleArray_[4];
+protected:
+     /// Allow OsiClp certain perks
+     friend class OsiClpSolverInterface;
+     /// And OsiCLP
+     friend class OsiCLPSolverInterface;
+     //@}
+};
+//#############################################################################
+/** A function that tests the methods in the ClpSimplex class. The
+    only reason for it not to be a member method is that this way it doesn't
+    have to be compiled into the library. And that's a gain, because the
+    library should be compiled with optimization on, but this method should be
+    compiled with debugging.
+
+    It also does some testing of ClpFactorization class
+ */
+void
+ClpSimplexUnitTest(const std::string & mpsDir);
+
+// For Devex stuff
+#define DEVEX_TRY_NORM 1.0e-4
+#define DEVEX_ADD_ONE 1.0
+#if defined(ABC_INHERIT) || defined(CBC_THREAD) || defined(THREADS_IN_ANALYZE)
+// Use pthreads
+#include <pthread.h>
+typedef struct {
+  double result;
+  //const CoinIndexedVector * constVector; // can get rid of
+  //CoinIndexedVector * vectors[2]; // can get rid of
+  void * extraInfo;
+  void * extraInfo2;
+  int status;
+  int stuff[4];
+} CoinThreadInfo;
+class CoinPthreadStuff {
+public:
+  /**@name Constructors and destructor and copy */
+  //@{
+  /** Main constructor
+  */
+  CoinPthreadStuff (int numberThreads=0,
+		    void * parallelManager(void * stuff)=NULL);
+  /// Assignment operator. This copies the data
+  CoinPthreadStuff & operator=(const CoinPthreadStuff & rhs);
+  /// Destructor
+  ~CoinPthreadStuff (  );
+  /// set stop start
+  inline void setStopStart(int value)
+  { stopStart_=value;}
+#ifndef NUMBER_THREADS 
+#define NUMBER_THREADS 8
+#endif
+  // For waking up thread
+  inline pthread_mutex_t * mutexPointer(int which,int thread=0) 
+  { return mutex_+which+3*thread;}
+#ifdef PTHREAD_BARRIER_SERIAL_THREAD
+  inline pthread_barrier_t * barrierPointer() 
+  { return &barrier_;}
+#endif
+  inline int whichLocked(int thread=0) const
+  { return locked_[thread];}
+  inline CoinThreadInfo * threadInfoPointer(int thread=0) 
+  { return threadInfo_+thread;}
+  void startParallelTask(int type,int iThread,void * info=NULL);
+  int waitParallelTask(int type, int & iThread,bool allowIdle);
+  void waitAllTasks();
+  /// so thread can find out which one it is 
+  int whichThread() const;
+  void sayIdle(int iThread);
+  //void startThreads(int numberThreads);
+  //void stopThreads();
+  // For waking up thread
+  pthread_mutex_t mutex_[3*(NUMBER_THREADS+1)];
+#ifdef PTHREAD_BARRIER_SERIAL_THREAD
+  pthread_barrier_t barrier_; 
+#endif
+  CoinThreadInfo threadInfo_[NUMBER_THREADS+1];
+  pthread_t abcThread_[NUMBER_THREADS+1];
+  int locked_[NUMBER_THREADS+1];
+  int stopStart_;
+  int numberThreads_;
+};
+void * clp_parallelManager(void * stuff);
+#endif
+#endif