ILOG CPLEX 11.0 User's Manual

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Using Optimization Callbacks on page 445 shows how to use callbacks to control a MIPsearch.Goals and Callbacks: a Comparison on page 469 compares the two different approaches.Advanced Presolve Routines on page 473 documents advanced aspects of presolve andaggregation more fully.Advanced MIP Control Interface on page 483 shows you how to exploit advanced featuresof MIP. It provides important additional information if you are using callbacks in yourapplication.Parallel Optimizers on page 491 explains how to exploit parallel optimizers in case yourhardware supports parallel execution.The Index on page 501 completes this manual.Examples OnlineFor the examples explained in the manual, you will find the complete code for the solutionin the examples subdirectory of the standard distribution of ILOG CPLEX, so that you cansee exactly how ILOG CPLEX fits into your own applications. Table 1 lists the examples inthis manual and indicates where to find them.Table 1ExamplesExample Source File In This Manualdietary optimization: building a model byrows (constraints) or by columns(variables), solving with IloCplex in C++dietary optimization: building a model byrows (constraints) or by columns (variables),solving with IloCplex in Javadietary optimization: building a model byrows (constraints) or by columns (variables),solving with Cplex in C#.NETdietary optimization: building a model byrows (constraints) or by columns(variables), solving with the Callable Librarylinear programming: starting from anadvanced basisilodiet.cppDiet.javaDiet.csdiet.cilolpex6.cpplpex6.cExample: Optimizing the Diet Problem in C++on page 65Example: Optimizing the Diet Problem in Javaon page 92Example: Optimizing the Diet Problem inC#.NET on page 107Example: Optimizing the Diet Problem in theCallable Library on page 125Example ilolpex6.cpp on page 194Example lpex6.c on page 19534 ILOG CPLEX 11.0 — USER’ S MANUAL
Table 1ExamplesExample Source File In This Manualnetwork optimization: using the CallableLibrarynetwork optimization: relaxing a networkflow to an LPquadratic programming: maximizing a QPquadratic programming: reading a QP froma formatted filequadratically constrained programming:QCPmixed integer programming: optimizing abasic MIPmixed integer programming: reading a MIPfrom a formatted filemixed integer programming: using specialordered sets (SOS) and priority ordersnetex1.cnetex2.ciloqpex1.cppQPex1.javaqpex1.cqpex2.cqcpex1.ciloqcpex1.cppQCPex1.javailomipex1.cppmipex1.cilomipex2.cppmipex2.cilomipex3.cppmipex3.cExample: Using the Network Optimizer with theCallable Library netex1.c on page 223Example: Network to LP Transformationnetex2.c on page 226Example: iloqpex1.cpp on page 236Example: QPex1.java on page 237Example: qpex1.c on page 237Example: Reading a QP from a File qpex2.c onpage 238Examples: QCP on page 251Examples: Optimizing a Simple MIP Problemon page 299Example: Reading a MIP Problem from a Fileon page 300Example: Using SOS and Priority on page 331cutting stock: using column generation cutstock.cpp What Is Column Generation? on page 374transport: piecewise-linear optimization transport.cpp Complete Program: transport.cpp on page 347food manufacturing 2: using logicalconstraintsfoodmanufac.cpp Using Logical Constraints: Food Manufacture 2on page 359early tardy scheduling etsp.cpp Early Tardy Scheduling on page 367input and output: using the messagehandlerlpex5.cExample: Callable Library Message Channelson page 152using query routines lpex7.c Example: Using Query Routines lpex7.c onpage 130using callbacksilolpex4.cpplpex4.ciloadmipex5.cppExample: Deriving the Simplex Callbackilolpex4.cpp on page 458Example: Using Callbacks lpex4.c on page 461Example: Controlling Cuts iloadmipex5.cpp onpage 462ILOG CPLEX 11.0 — USER’ S MANUAL 35
Page 1 and 2: ILOG CPLEX 11.0User’s ManualSepte
Page 3 and 4: C O N T E N T STable of ContentsILO
Page 5 and 6: Building the Model by Column . . .
Page 8: Program Description . . . . . . . .
Page 13 and 14: Cholesky Factor in the Log File . .
Page 15: Examples: QCP . . . . . . . . . . .
Page 19 and 20: Chapter 18 Using Piecewise Linear F
Page 21 and 22: Representing the Data. . . . . . .
Page 23 and 24: Reading and Writing MPS Files . . .
Page 25 and 26: Protected Variables in Presolve Red
Page 27 and 28: P R E F A C EMeet ILOG CPLEXILOG CP
Page 29 and 30: When a linear optimization problem
Page 31 and 32: ◆◆◆◆implemented in ILOG CPL
Page 33: Solution Pool: Generating and Keepi
Page 38 and 39: ◆◆◆◆◆◆Overview of the A
Page 40 and 41: Wolsey, Laurence A., Integer Progra
Page 43 and 44: C H A P T E R1ILOG Concert Technolo
Page 45 and 46: Compiling and LinkingCompilation an
Page 47 and 48: The class IloNumVar provides method
Page 49 and 50: IloModel object itself and added to
Page 51 and 52: Table 1.1 Concert Technology Modeli
Page 53 and 54: Choosing an OptimizerSolving the ex
Page 55 and 56: Table 1.4 on page 55 summarizes the
Page 57 and 58: Dynamic control of the solution pro
Page 59 and 60: However, querying solution values v
Page 61 and 62: parameter for the getQuality method
Page 63 and 64: model2.add(model1);model2.add(IloCo
Page 65 and 66: Example: Optimizing the Diet Proble
Page 67 and 68: Concert Technology, as demonstrated
Page 69 and 70: method IloModel::add returns the mo
Page 71 and 72: C H A P T E R2ILOG Concert Technolo
Page 73 and 74: Licenses in a Java ApplicationILOG
Page 75 and 76: Models that consist only of such co
Page 77 and 78: The special case of linear expressi
Page 79 and 80: IloObjective obj = cplex.add(cplex.
Page 81 and 82: Solving the ModelOnce you have crea
Page 83 and 84: 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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C H A P T E R3ILOG Concert Technolo
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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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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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