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diff --git a/build/Bonmin/include/coin/ClpSimplex.hpp b/build/Bonmin/include/coin/ClpSimplex.hpp new file mode 100644 index 0000000..bab4506 --- /dev/null +++ b/build/Bonmin/include/coin/ClpSimplex.hpp @@ -0,0 +1,1797 @@ +/* $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 |