ILOG CPLEX 11.0 User's Manual

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Iill-conditionedbasis 193factors in 193maximum dual residual and 193problem 190IloAdd template class (C++ API) 68IloAddable class (Java API)active model 78modeling objects and 74IloAlgorithm::Exception class (C++ API) 63IloAlgorithm::Status enumeration (C++ API) 57IloArray template class (C++ API) 49IloColumn classand example (Java API) 93IloColumnArray class (Java API) 92IloConstraint class (C++ API) 51IloConversion class (C++ API) 47, 51, 62IloConversion class (Java API) 95IloCplex classgetBasisStatus method (Java API) 89getBasisStatuses method (C++ API) 59getBoundSA method (C++ API) 59getCplexStatus method (C++ API) 58, 60getDefault method (C++ API) 55getDual method (C++ API) 59getDuals method (C++ API) 59getMax method (C++ API) 55getMin method (C++ API) 55getObjSA method (C++ API) 59getObjValue method (C++ API) 59getParam method (C++ API) 55getQuality method 188, 205getQuality method (C++ API) 60getReducedCost method (C++ API) 59getReducedCosts method (C++ API) 59getRHSSA method (C++ API) 59getSlack method (C++ API) 59getSlacks method (C++ API) 59getStatus method (C++ API) 57getStatuses method 194getValue method (C++ API) 58getValues method (C++ API) 59IloMPModeler and (Java API) 74isDualFeasible method (C++ API) 58isPrimalFeasible method (C++ API) 58modeling objects and (Java API) 74notifying about changes to (C++ API) 61objects in user application (C++ API) 44PrimalPricing (Java API) 86setDefaults method (C++ API) 56setParam method (C++ API) 56solve method (C++ API) 52, 57, 58, 59, 60, 61, 62, 69writeBasis method 188IloCplex::Algorithm enumeration 53IloCplex::BarStartAlg 211IloCplex::BasisStatus enumeration (C++ API) 59IloCplex::BoolParam enumeration (C++ API) 55IloCplex::Exception class 141getStatus method 141IloCplex::Exception class (C++ API) 63IloCplex::IntParam enumeration (C++ API) 55IloCplex::Kappa 188IloCplex::NumParam enumeration (C++ API) 55IloCplex::Quality enumeration (C++ API) 60IloCplex::Reduce 385IloCplex::StringParam enumeration (C++ API) 55IloCplexModeler interfacemodeling objects (Java API) 74IloCPModeler class (Java API) 74IloEnv class 46end method (C++ API) 46IloException classgetMessage method 141IloExpr C++ class 47ILOG License Managerexamples 157invoking 156ILOG License Manager (ILM) 155CPLEX (C++ API) and 44types of 156IloLPMatrix class (Java API) 90IloMaximize C++ function 48IloMinimize C++ function 48, 68IloModel C++ class 48IloModel classadd method (C++ API) 49, 61add method (Java API) 94remove method (C++ API) 49, 61516 ILOG CPLEX 11.0 — USER’ S MANUAL
emove method (Java API) 94IloModeler classbasic modeling (Java API) 76creating modeling objects (Java API) 74creating variables (Java API) 76IloMPModeler classcreating variables (Java API) 76IloMPModeler class (Java API) 74delete method 94IloNumArray C++ class 49IloNumExpr classobjective and (Java API) 78ranged constraints and (Java API) 77variables and (Java API) 76IloNumVar C++ class 46, 51IloNumVar classmodeling objects and (Java API) 74IloNumVarArray C++ class 47IloNumVarclassextension of IloNumExpr (Java API) 76IloObjective C++ class 51IloObjective classaddable objects (Java API) 78as modeling object (C++ API) 51declaring (C++ API) 47modeling by column (Java API) 91setExpr method in QP term 233IloObjectiveSense classexample (Java API) 78maximizing (Java API) 78minimizing (Java API) 78objective function and (Java API) 78iloqpex1.cpp exampleexampleiloqpex1.cpp 236IloRange classadding constraints (C++ API) 48linear constraints and (C++ API) 51modeling by column (Java API) 91modeling objects and (Java API) 78IloSemiContVar class 51IloSolver as factory (Java API) 72IloSOS1 C++ class 51IloSOS2 C++ class 51ImplBd parametercontrolling cuts 274implied bound cut 273implied bound cutsdefined 273include file 141incumbentnode 267solution 267solution pool and 305incumbent callback 489deterministic search and 328dynamic search and 328filtering solution pool 324solution pool and 328index number (C API) 118indicator constraint 349definition 355restrictions 357indicator variable 357indicators: see indicator constraintinfeasibilitybarrier optimizer and 216conflicts and 391diagnosing in network flows 226displaying on screen 191dual 213, 216interpreting results 192maximum bound 192maximum reduced-cost 192network flow 219network optimizer and 226norms 204primal 205, 213, 216ratio in barrier log file 205repairing 409reports 189scaling and 190unboundedness and (LP) 191unscaled 190Infeasible return status (C++ API) 58Infeasible return status (Java API) 81infeasible solutionaccessing information (Java API) 89analyzing (C++ API) 59InfeasibleOrUnboundedILOG CPLEX 11.0 — USER’ S MANUAL 517
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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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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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Page 467 and 468: Interaction Between Callbacks and I
Page 469 and 470: C H A P T E R31Goals and Callbacks:
Page 471 and 472: The only functionality that is not
Page 473 and 474: C H A P T E R32Advanced Presolve Ro
Page 475 and 476: once. All of the node relaxation so
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Page 479 and 480: problem, the problem has a presolve
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
Page 487 and 488: Cut CallbackThe next example we con
Page 489 and 490: problem. Since branching involves a
Page 491 and 492: C H A P T E R34Parallel OptimizersT
Page 493 and 494: Threads and Performance Considerati
Page 495 and 496: 3. Call the parallel optimizer with
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
Page 503 and 504: ole in converting LP to network flo
Page 505 and 506: emoving from basis (C++ API) 62repr
Page 507 and 508: CPX_PARAM_PCPX_PARAM_POLISHTIMEsolu
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: getDuals method (Java API) 88getMax
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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