ILOG CPLEX 11.0 User's Manual

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variable type (C++ API) 62channel example 153character string length requirements (C API) 119check.c CPLEX file 120Cholesky factor 207barrier iteration 198barrier log file and 204clique cutscounting 273definition 272Cliques parametercontrolling cuts 274ClockType parameterparallel processing and 500cloneK.log 500clones 499log files 500threads and 499closingapplication (C API) 114application (network) 224environment (C API) 114environment (network) 224log files 149CoeRedInd parameterMIP preprocessing 278columndense 214density 210index number (C API) 118name (C API) 118nonzeros parameter and density 210nonzeros parameter and instability 214referencing (C API) 118column generationbasis and 374cutting plane method and 374reduced cost and (example) 376reduced cost to select next variable 374columnwise modeling (C API) 126columnwise modeling (C++ API) 66columnwise modeling (Java API)IloMPModeler and 75objective and 91ranges and 91complementarity 198barrier optimizer and 198convergence tolerance 215unbounded models and 215Component Libraries (definition) 28Concert Technologyaccessing parameter values (C++ API) 55application development steps (C++ API) 45creating application (C++ API) 45description 28design (C++ API) 44error handling (C++ API) 63solving problem with (C++ API) 44using (C++ API) 43 to 70writing programs with (C++ API) 43concurrent optimizer 496determinism and 493licensing issues 492non-default parameters and 175option for (C++ API) 54parallel implementation 493parallel processing and 498root relaxation and 498threads and 493cone (SOCP) 242conflictcomparing IIS 393definition 391groups in 406conflict refiner 391C++ API example 403Interactive Optimizer example 394possible status 401proved status 401constraintadding with user-written callback 487convex quadratic 239creating ranged (Java API) 74cuts as 271indicator 355lazy 419, 487logical 349modeling linear (C++ API) 51quadratic 239ranged (Java API) 77504 ILOG CPLEX 11.0 — USER’ S MANUAL
emoving from basis (C++ API) 62representing with IloRange (C++ API) 48violation 193constructing arrays of variables (Java API) 92continuous piecewise linear 340continuous relaxation (Java API) 84continuous relaxation (MIP) 265continuous relaxation subproblem 428control callbackdefinition 451determinism and 453opportunistic search and 453types of 451conventionscharacter strings (C API) 119naming 146notation 36numbering 142, 144numbering rows, columns 141convergence tolerancebarrier algorithm and 175definitioin 207effect of tightness 214performance and 207convert CPLEX utility 147convertingerror code to string 223file formats 147network-flow model to LP 224network-flow problem to LP 225, 226convexquadratic constraints and 240convex quadratic constraint 239cover cuts 272counting 273defined 272Covers parametercontrolling cover cuts 274CPLEXComponent Libraries 28core (C API) 110licensing (C++ API) 44parameters (C++ API) 55cplex.h header fileC API 124extern statements in 123in an application 141macros for pointers in 121cplex.log filechanging name 202clone logs 500default name 149, 180CPX_ALG_AUTO symbolic constant 298CPX_ALG_CONCURRENT symbolic constant 298CPX_ALG_DUAL symbolic constant 298CPX_ALG_HYBNETOPT symbolic constant 298CPX_ALG_PRIMAL symbolic constant 298CPX_ALG_SIFTING symbolic constant 298CPX_INTEGER_INFEASIBLE 489CPX_PARAM_ACPX_PARAM_ADVINDMIP start 280presolve and advanced start 479solution polishing and 277CPX_PARAM_AGGINDMIP preprocessing 278CPX_PARAM_BCPX_PARAM_BARSTARTALGbarrier starting algorithm 211CPX_PARAM_BBINTERVALcontrolling branch and cut 267CPX_PARAM_BNDSTRENINDMIP preprocessing 278CPX_PARAM_BRDIRcontrolling branch and cut 267CPX_PARAM_BTTOLcontrolling branch and cut 267CPX_PARAM_CCPX_PARAM_CLIQUEScontrolling cuts 274CPX_PARAM_CLOCKTYPEexample of parameter checking 124parallel processing and 500CPX_PARAM_COEREDINDILOG CPLEX 11.0 — USER’ S MANUAL 505
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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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Page 455 and 456: It is not customary to write such a
Page 457 and 458: IloCplex::Callback mycallback = cpl
Page 459 and 460: Here is the previous sample of code
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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: ole in converting LP to network flo
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 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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