ILOG CPLEX 11.0 User's Manual

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MIP preprocessing 278CPX_PARAM_COVERScontrolling cuts 274CPX_PARAM_CUTLOconflict refiner and 392FeasOpt and 410CPX_PARAM_CUTUPconflict refiner and 392FeasOpt and 410CPX_PARAM_DCPX_PARAM_DATACHECKentering problem data and 119CPX_PARAM_DEPINDbarrier 209LPs and 177CPX_PARAM_DISJCUTScontrolling cuts 274CPX_PARAM_ECPX_PARAM_EPAGAPlimiting MIP optimization 264CPX_PARAM_EPGAPlimiting MIP optimization 264CPX_PARAM_EPOPT 193CPX_PARAM_EPRHS 192CPX_PARAM_FCPX_PARAM_FLOWCOVERScontrolling cuts 274CPX_PARAM_FLOWPATHScontrolling cuts 274CPX_PARAM_FRACCUTScontrolling cuts 274CPX_PARAM_GCPX_PARAM_GUBCOVERScontrolling cuts 274CPX_PARAM_ICPX_PARAM_IMPLBDcontrolling cuts 274CPX_PARAM_INTSOLLIMlimiting MIP optimization 264CPX_PARAM_LCPX_PARAM_LPMETHODchoosing LP optimizer 172network flow 221parallel processing and 496CPX_PARAM_MCPX_PARAM_MEMORYEMPHASISbarrier 208conserving memory 183final factor after preprocessing 177presolve and 177CPX_PARAM_MIPCBREDLPbranch callbacks and 488callback arguments and 486heuristic callbacks and 486incumbent callback and 489presolved and original problem 485user defined cuts and 488CPX_PARAM_MIRCUTScontrolling cuts 274CPX_PARAM_NCPX_PARAM_NODEFILEINDeffect on storage 294node files and 294CPX_PARAM_NODELIMlimiting MIP optimization 264CPX_PARAM_NODESELcontrolling branch and cut 267CPX_PARAM_NUMERICALEMPHASISbarrier 212LP 185506 ILOG CPLEX 11.0 — USER’ S MANUAL
CPX_PARAM_PCPX_PARAM_POLISHTIMEsolution polishing 276CPX_PARAM_PREINDMIP preprocessing 278CPX_PARAM_PRELINEARadvanced MIP control and 487advanced presolve 478user cut pools 421user defined cuts 421CPX_PARAM_PREPASSMIP preprocessing 278CPX_PARAM_PROBEMIP 271CPX_PARAM_QPMETHODnetwork flow and quadratic objective 222CPX_PARAM_RCPX_PARAM_REDUCEadvanced presolve 477infeasible problems and 478lazy constraints and 421lazy constraints and advanced MIP control 487MIP preprocessing 278optimal basis and 479presolve and problem modifications 481problem modifications and 479unbounded problems and 478CPX_PARAM_RELAXPREINDadvanced presolve 474MIP preprocessing 278CPX_PARAM_REPAIRTRIESMIP starts and 279CPX_PARAM_REPEATPRESOLVEMIP preprocessing 278purpose 279CPX_PARAM_SCPX_PARAM_SCAIND 182CPX_PARAM_SCRINDerror checking and 120example lpex6.c 195example with callbacks 461managing input and output 152network flow 223programming practices and 141repeated singularities and 188CPX_PARAM_SCRIND parameterdata checking and 120reporting repeated singularities 188CPX_PARAM_STARTALGcontrolling algorithm in initial relaxation (MIP) 270initial subproblem and 298parallel processing and barrier 498CPX_PARAM_SUBALGcontrolling algorithm at nodes 270node relaxations and 298CPX_PARAM_SUBMIPNODELIMsolution polishing and 277CPX_PARAM_TCPX_PARAM_TILIMlimiting MIP optimization 264solution polishing and 277CPX_PARAM_TRELIMeffect on storage 295limiting MIP optimization 264node files and 294CPX_PARAM_VCPX_PARAM_VARSELcontrolling branch and cut 267CPX_PARAM_WCPX_PARAM_WORKDIRbarrier 209node file subdirectory 295node files and 294CPX_PARAM_WORKMEMbarrier 208node files and 294CPX_PARAM_ZEROHALFCUTScontrolling zero-half cuts 274CPX_PREREDUCE_DUALONLY 477ILOG CPLEX 11.0 — USER’ S MANUAL 507
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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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then ILOG CPLEX will carry out such
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-a 1 + a 2 - a 8 - a 9 + a 14 = 0-
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C H A P T E R12Solving Problems wit
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convex QPs, Q must be positive semi
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A similar Java program using Concer
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◆ qp indicates that you want ILOG
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RootAlg parameter (QPMETHOD in the
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ilolpex1.cpp counterpart. Here the
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C H A P T E R13Solving Problems wit
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Figure 13.2y(0, 1)(-1, 0)d(1, 0)xc(
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Detecting Problem TypeILOG CPLEX de
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COLUMNSMARK0000 'MARKER''INTORG'C15
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C180 R100 159C180 R119 200C180 R120
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QMATRIXC158 C158 1C158 C189 0.5C189
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◆From the Callable Library, use t
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Part IVDiscrete OptimizationThis pa
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Stating a MIP ProblemA mixed intege
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sections does not matter. To enter
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◆milp, miqp, or miqcpindicating t
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1. finding a succession of improvin
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easonable amount of computation tim
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If the solution to the relaxation s
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anches taken so far in this dive. S
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directives about the order in which
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◆ Parameters Affecting Cuts on pa
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◆●●IloCplex.getNcuts in the J
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Relaxation Induced Neighborhood Sea
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Table 14.10Parameters for Controlli
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Library). If this process succeeds,
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After you have solved a MIP, you wi
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Table 14.13Settings of the MIP Disp
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ILOG CPLEX also logs its addition o
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◆ Parent specifies the NodeID of
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parameter settings are also worth c
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When you set a MIP cutoff value, IL
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memory. It also includes several op
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◆◆●●at problem modification
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◆In the Callable Library, use the
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●●Then call CPXprimopt to optim
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◆ The Incumbent and the Solution
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c17: x1 - y11 >= 0c18: x1 - y21 >=
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Populating the Solution PoolILOG CP
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NodesCuts/Node Left Objective IInf
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Note: The parameter to limit the nu
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Example: Using Populate after MIP O
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Limitations Due to Numeric Difficul
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Examining the Solution PoolIn the I
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For a sample of these methods or ro
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◆◆In the Callable Library (C AP
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Then display the objective value of
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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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Page 457 and 458: IloCplex::Callback mycallback = cpl
Page 459 and 460: Here is the previous sample of code
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Page 469 and 470: C H A P T E R31Goals and Callbacks:
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Page 473 and 474: C H A P T E R32Advanced Presolve Ro
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Page 481 and 482: Modifying a ProblemThis section bri
Page 483 and 484: C H A P T E R33Advanced MIP Control
Page 485 and 486: value strictly greater than one. It
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Page 491 and 492: C H A P T E R34Parallel OptimizersT
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Page 497 and 498: optimizer turns out to be the faste
Page 499 and 500: 0 0 3383.7784 16 4505.0000 Cuts: 35
Page 501 and 502: I N D E XIndexAabsolute objective d
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Page 505: emoving from basis (C++ API) 62repr
Page 509 and 510: CPXcreateprob 467CPXcreateprob rout
Page 511 and 512: CPXwcpxwarning message channel 151C
Page 513 and 514: empty goal 431, 435end methodIloEnv
Page 515 and 516: getDuals method (Java API) 88getMax
Page 517 and 518: emove method (Java API) 94IloModele
Page 519 and 520: ecords singularities 188relocating
Page 521 and 522: from 218head 218sink 219source 219s
Page 523 and 524: control callbacks and 453query call
Page 525 and 526: primal reduction 385primal simplex
Page 527 and 528: eturn statusBounded (Java API) 81Er
Page 529 and 530: solvingdiet problem (Java API) 82mo
Page 531 and 532: arrier 208WorkMem 293WorkMem parame
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