ILOG CPLEX 11.0 User's Manual

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◆●●IloCplex.getNcuts in the Java API;Cplex.GetNcuts in the .NET API.For Callable Library users, use the routine CPXgetnumcuts.Parameters Affecting CutsParameters control the way each class of cuts is used. Those parameters are listed inTable 14.9.Table 14.9 Parameters for Controlling CutsCut TypeInteractive CommandConcert TechnologyParameterCallable Library ParameterClique set mip cuts cliques Cliques CPX_PARAM_CLIQUESCover set mip cuts covers Covers CPX_PARAM_COVERSDisjunctive set mip cuts disjunctive DisjCuts CPX_PARAM_DISJCUTSFlow Cover set mip cuts flowcovers FlowCovers CPX_PARAM_FLOWCOVERSFlow Path set mip cuts pathcut FlowPaths CPX_PARAM_FLOWPATHSGomory set mip cuts gomory FracCuts CPX_PARAM_FRACCUTSGUB Cover set mip cuts gubcovers GUBCovers CPX_PARAM_GUBCOVERSImplied Bound set mip cuts implied ImplBd CPX_PARAM_IMPLBDMixed Integer Rounding (MIR) set mip cuts mircut MIRCuts CPX_PARAM_MIRCUTSZero-Half set mip cuts zerohalfcut ZeroHalfCuts CPX_PARAM_ZEROHALFCUTSThe default value of each of those parameters is 0 (zero). By default, ILOG CPLEXautomatically decides how often (if at all) it should try to generate that class of cut. A settingof -1 indicates that no cuts of the class should be generated; a setting of 1 indicates that cutsof the class should be generated moderately; and a setting of 2 indicates that cuts of the classshould be generated aggressively. For clique cuts, cover cuts, and disjunctive cuts, a settingof 3 is permitted, which indicates that the specified type of cut should be generated veryaggressively.In the Interactive Optimizer, the command set mip cuts all i applies the value i toall types of cut parameters. That is, you can set them all at once.The CutsFactor parameter controls the number of cuts ILOG CPLEX adds to the model.The problem can grow to CutsFactor times the original number of rows in the model (or inthe presolved model, if the presolver is active). Thus, a CutsFactor of 1.0 would mean thatno cuts will be generated, which may be a more convenient way of turning off all cuts thansetting them individually. The default CutsFactor value of 4.0 works well in most cases, as274 ILOG CPLEX 11.0 — USER’ S MANUAL
it allows a generous number of cuts while in rare instances it also serves to limit uncheckedgrowth in the problem size.The AggCutLim parameter controls the number of constraints allowed to be aggregated forgenerating MIR and flow cover cuts.The FracPass parameter controls the number of passes for generating Gomory fractionalcuts. This parameter will not have any effect if the parameter for set mip cuts gomoryhas a nondefault value.The FracCand parameter controls the number of variable candidates to be considered forgenerating Gomory fractional cuts.HeuristicsIn ILOG CPLEX, a heuristic is a procedure that tries to produce good or approximatesolutions to a problem quickly but which lacks theoretical guarantees. In the context ofsolving a MIP, a heuristic is a method that may produce one or more solutions, satisfying allconstraints and all integrality conditions, but lacking an indication of whether it has foundthe best solution possible.ILOG CPLEX provides these families of heuristics to find integer solutions at nodes(including the root node) during the branch & cut procedure:◆ Node Heuristic on page 275◆ Relaxation Induced Neighborhood Search (RINS) Heuristic on page 276◆ Solution Polishing on page 276Being integrated into branch & cut, these heuristic solutions gain the same advantagestoward a proof of optimality as any solution produced by branching, and in many instances,they can speed the final proof of optimality, or they can provide a suboptimal buthigh-quality solution in a shorter amount of time than by branching alone. With defaultparameter settings, ILOG CPLEX automatically invokes the heuristics when they seemlikely to be beneficial.Node HeuristicThe node heuristic employs techniques to try to construct a feasible solution from the current(fractional) branch & cut node. This feature is controlled by the parameter HeurFreq. At itsdefault value of 0 (zero), ILOG CPLEX dynamically sets the frequency with which theheuristic is invoked. The setting -1 turns the feature off. A positive value specifies thefrequency (in node count) with which the heuristic will be called. For example, if theHeurFreq parameter is set to 20, then the node heuristic will be applied at node 0, node 20,node 40, and so on.ILOG CPLEX 11.0 — USER’ S MANUAL 275
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ILOG CPLEX 11.0User’s ManualSepte
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C O N T E N T STable of ContentsILO
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Building the Model by Column . . .
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Program Description . . . . . . . .
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Cholesky Factor in the Log File . .
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Examples: QCP . . . . . . . . . . .
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Chapter 18 Using Piecewise Linear F
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Representing the Data. . . . . . .
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Reading and Writing MPS Files . . .
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Protected Variables in Presolve Red
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P R E F A C EMeet ILOG CPLEXILOG CP
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When a linear optimization problem
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◆◆◆◆implemented in ILOG CPL
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Solution Pool: Generating and Keepi
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Table 1ExamplesExample Source File
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◆◆◆◆◆◆Overview of the A
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Wolsey, Laurence A., Integer Progra
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C H A P T E R1ILOG Concert Technolo
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Compiling and LinkingCompilation an
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The class IloNumVar provides method
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IloModel object itself and added to
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Table 1.1 Concert Technology Modeli
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Choosing an OptimizerSolving the ex
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Table 1.4 on page 55 summarizes the
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Dynamic control of the solution pro
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However, querying solution values v
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parameter for the getQuality method
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model2.add(model1);model2.add(IloCo
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Example: Optimizing the Diet Proble
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Concert Technology, as demonstrated
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method IloModel::add returns the mo
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Licenses in a Java ApplicationILOG
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Models that consist only of such co
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The special case of linear expressi
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IloObjective obj = cplex.add(cplex.
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Solving the ModelOnce you have crea
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IloCplex cplex = new IloCplex();Ilo
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IloCplex.setParam(IloCplex.IntParam
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Thus, the suggested method for sett
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Basis InformationWhen solving an LP
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where A is a sparse matrix. A spars
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The example starts by evaluating th
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IloMPModeler.setLinearCoefs, and Il
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◆What is the purpose (the objecti
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Step 3Create the modelGo to the com
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Step 9Add nutritional constraintsGo
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Step 14Display the solutionGo to th
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Step 18Enclose the application in t
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C H A P T E R4ILOG CPLEX Callable L
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◆◆file reading and writing rout
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◆◆If data already exist in MPS,
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For example, let’s look at the sy
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As you modify a problem object thro
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However, ILOG CPLEX updates the nam
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whether an optimization should be a
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Callable Library have names greater
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◆CPXsetstrparam accepts arguments
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Here’s another way to visualize a
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program—can discover the length o
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Part IIProgramming ConsiderationsTh
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◆ Test Data on page 135◆ Choose
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◆ Use the MIP optimizer if the pr
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Choose Clarity First, Efficiency La
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1. If you can reproduce the behavio
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indices, errors will occur. Therefo
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Understanding File FormatsThe refer
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definitions that it finds. The firs
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◆◆IloXmlReader creates a reader
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Controlling Message ChannelsIn both
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more calls to the message handling
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Concert Technology Message Channels
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Types of ILM Runtime LicensesILM ru
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char *inststr = NULL;char *envstr =
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The registerLicense Method for Java
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not correct that problem either. In
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In the Interactive Optimizer, you c
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For models not in the current worki
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◆◆In the .NET API, pass an inst
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C H A P T E R9Solving LPs: Simplex
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The symbolic names for these settin
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When no candidates are present, the
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solution; but examination of soluti
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Then to later read an advanced basi
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Table 9.5 PPriInd Parameter Setting
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ILOG CPLEX ignores the coefficients
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Numeric DifficultiesILOG CPLEX is d
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esult is the same as would be obtai
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automatically perturbs the variable
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4. Consider alternate scalings.You
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smaller in absolute value than the
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exceptions are caught as well, and
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C H A P T E R10Solving LPs: Barrier
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The ILOG CPLEX Barrier Optimizer is
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And then you call the solution rout
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Here is an example of a log file fo
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If solution values are large in abs
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Normalized errors, for example, rep
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◆ workmem in the Interactive Opti
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est order. It may require more time
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choice of starting-point heuristic
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To see the current value of the col
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C H A P T E R11Solving Network-Flow
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◆◆l a , the lower bound, sets t
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of the objective function calculate
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Page 227 and 228: C H A P T E R12Solving Problems wit
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Page 231 and 232: A similar Java program using Concer
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Page 239 and 240: C H A P T E R13Solving Problems wit
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Page 243 and 244: Detecting Problem TypeILOG CPLEX de
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Page 247 and 248: C180 R100 159C180 R119 200C180 R120
Page 249 and 250: QMATRIXC158 C158 1C158 C189 0.5C189
Page 251 and 252: ◆From the Callable Library, use t
Page 253: Part IVDiscrete OptimizationThis pa
Page 256 and 257: Stating a MIP ProblemA mixed intege
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Page 262 and 263: 1. finding a succession of improvin
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Page 272 and 273: ◆ Parameters Affecting Cuts on pa
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Page 282 and 283: After you have solved a MIP, you wi
Page 284 and 285: Table 14.13Settings of the MIP Disp
Page 286 and 287: ILOG CPLEX also logs its addition o
Page 288 and 289: ◆ Parent specifies the NodeID of
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Page 294 and 295: memory. It also includes several op
Page 296 and 297: ◆◆●●at problem modification
Page 298 and 299: ◆In the Callable Library, use the
Page 300 and 301: ●●Then call CPXprimopt to optim
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Page 306 and 307: Populating the Solution PoolILOG CP
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Page 310 and 311: Note: The parameter to limit the nu
Page 312 and 313: Example: Using Populate after MIP O
Page 314 and 315: Limitations Due to Numeric Difficul
Page 316 and 317: Examining the Solution PoolIn the I
Page 318 and 319: For a sample of these methods or ro
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◆ If you want to filter solutions
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●●●In the C++ API, use the me
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NAME locationRNGFILTER f2 -inf 0tra
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continuous, a model containing one
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◆The routine setPriorities sets t
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What Are Semi-Continuous Variables?
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With that model, now the applicatio
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Piecewise Linearity in ILOG CPLEXSo
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Reminder: It may help you understan
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overlaps with an endpoint of two ot
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Figure 18.4400003000020000100000Fig
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in demand; in terms of this model,
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348 ILOG CPLEX 11.0 — USER’ S M
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What Are Logical Constraints?For IL
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◆◆Cplex.NotCplex.IfThenAgain, t
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In Concert Technology applications,
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Describing the ProblemThe problem i
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Notice that how much to use and buy
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Then use a for-loop to add the cons
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These lines (the action of the if-s
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Describing the ProblemThe problem i
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Stating Precedence ConstraintsIn ea
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Solving the ProblemAn emphasis on f
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What Is Column Generation?In colloq
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Solving this model with all columns
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Likewise, the application adds an a
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Pattern Generator ModelThe submodel
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Complete ProgramYou can see the ent
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your problem formulation caused thi
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To control the types of reductions
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What Is Unboundedness?Any class of
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Callable Library use the advanced r
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the conflict to arrive at a minimal
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the IIS finder can, and often more.
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Then you will see results like thes
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Now view the entire conflict with t
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The constraints in conflict with th
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If a model contains more than one c
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Immediately after that statement, i
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◆ Groups in the Conflict Refiner
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The infeasibility on which FeasOpt
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Now the following lines invoke Feas
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suggests decreasing the upper bound
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C H A P T E R28User-Cut and Lazy-Co
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However, there is an important dist
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◆◆Through the routine CPXreadco
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Here is an example of an MPS file e
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C H A P T E R29Using GoalsThis chap
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How Goals Are Implemented in Branch
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In the Java API, a Fail goal is ret
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This return statement returns an An
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The Goal StackTo understand how goa
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soon as they are enclosed in a goal
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The global cut goal for lhs[i] ≤
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If a node has multiple evaluators a
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As this example is an extension of
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C H A P T E R30Using Optimization C
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Reference Documents about Informati
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Query or Diagnostic CallbacksQuery
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● Cplex.DisjunctiveCutCallback in
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◆◆●●Cplex.HeuristicCallback
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It is not customary to write such a
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IloCplex::Callback mycallback = cpl
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Here is the previous sample of code
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◆◆cbdata, a pointer to ILOG CPL
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conventions of the Interactive Opti
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een added, it can be deleted by cal
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Interaction Between Callbacks and I
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C H A P T E R31Goals and Callbacks:
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The only functionality that is not
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C H A P T E R32Advanced Presolve Ro
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once. All of the node relaxation so
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- 5x1, + x2 ≤ 0 2y1, + y2, ≥ 1x
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problem, the problem has a presolve
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Modifying a ProblemThis section bri
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C H A P T E R33Advanced MIP Control
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value strictly greater than one. It
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Cut CallbackThe next example we con
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problem. Since branching involves a
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C H A P T E R34Parallel OptimizersT
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Threads and Performance Considerati
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3. Call the parallel optimizer with
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optimizer turns out to be the faste
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0 0 3383.7784 16 4505.0000 Cuts: 35
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I N D E XIndexAabsolute objective d
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ole in converting LP to network flo
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emoving from basis (C++ API) 62repr
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CPX_PARAM_PCPX_PARAM_POLISHTIMEsolu
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CPXcreateprob 467CPXcreateprob rout
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CPXwcpxwarning message channel 151C
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empty goal 431, 435end methodIloEnv
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getDuals method (Java API) 88getMax
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emove method (Java API) 94IloModele
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ecords singularities 188relocating
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from 218head 218sink 219source 219s
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control callbacks and 453query call
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primal reduction 385primal simplex
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eturn statusBounded (Java API) 81Er
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solvingdiet problem (Java API) 82mo
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arrier 208WorkMem 293WorkMem parame
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