ILOG CPLEX 11.0 User's Manual

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IloCplex C++ class 56setExpr methodIloObjective class 233setOut method 149setParam methodIloCplex C++ class 56settingalgorithm in LP (C++ API) 53all default parameters (C API) 125all default parameters (C++ API) 56callbacks to null (C API) 125callbacks to null (C++ API) 56parameters (C API) 124parameters in C++ API 56sifting 174simplexcolumn generation and 374dual 173feasibility tolerance in MIP 292optimizer 199pricing phase and 374primal 174singularity 188slackaccessing bound violations in (C++ API) 60accessing in constraints in active model (Java API) 82accessing slack variables in constraints (C++ API) 59accessing slack variables in objective (C++ API) 59as indicator of ill-conditioning 193as reduced cost in infeasibility analysis 193example CPXgetslack 299in primal formulation (Barrier) 198in summary statistics 192infeasibilities as bound violations and 193infeasibility in dual associated with reduced costs 192initial norms and 181maximum bound violation and (Java API) 90meaning in infeasible primal or dual LP 192pivoted in when constraint is removed (C++ API) 62primal bound error in solution quality (Barrier) 206reducing computation of steepest edge pricing 181role in inequality constraints (Barrier) 204role in infeasibility analysis 193row complementarity in solution quality (Barrier) 206steepest edge and (dual) 180steepest edge and (primal) 181using primal variables instead 182variable needed in basis (Network) 225variables and primal variables (dual) 182SOCP second-order cone program 242solutionaccessing quality information (C++ API) 60accessing values of (C++ API) 58alternative (MIP) 302basic infeasible primal 189basis 199complementary 198differences between barrier, simplex 199example QP 237, 238feasible in MIPs 280incumbent 267infeasible basis 216midface 199nonbasis 199pool (MIP) 302quality 205, 212serializing 147supplying first integer in MIPs 279using advanced presolved 479verifying 212XML representation of 147solution goal 432solution polishing 276solution poolchanging fixed problem and 319definition 302diversity filters and 324filter files 326fixed problem and 319incumbent in 305MIP start and 318populating 306range filters and 325reference set and 324replaced solutions 316stopping criteria 309writing MIP start file 318solve callback 490solve methodIloCplex C++ class 52, 57, 58, 59, 60, 61, 62, 69528 ILOG CPLEX 11.0 — USER’ S MANUAL
solvingdiet problem (Java API) 82model (C++ API) 52single LP (Java API) 84subsequent LPs or QPs in a MIP (Java API) 85sparse matrixIloLPMatrix and (Java API) 91special ordered set (SOS)role in model (Java API) 75type 1 (C++ API) 51type 2 (C++ API) 51using 329weights in 331stalling 188starting algorithmcallbacks and 470goals and 470parallel processing 498static variables (C API) 114status variables, using 461steepest-edge pricing 181, 294step in piecewise linear function 340stopping criterioncallbacks and 462optimization (MIP) 263solution pools and 309strong branching 290SubMIPNodeLim parameterRINS and 276solution polishing and 277subproblemdefinition (MIP) 270summary statistics 192suppressing output to the screen 154surplus argument (C API) 128symbolic constants (C API) 117, 124Ttail 218terminatingbecause of singularities 188MIP optimization 263network optimizer iterations 221The 264threads 491clones 499parallel optimizers 491performance and 492thread-safe (C API) 114TiLim parameterlimiting MIP optimization 264solution polishing and 276time limitconcurrent optimizer and 175deterministic search and 494effects all algorithms invoked by concurrent optimizer175possible reason for Unknown return status (C++ API) 58possible reason for Unknown return status (Java API) 81TiLim parameter (MIP) 262toleranceabsolute objective difference and 266absolute optimality 291advice about 464complementarity convergence, default of 215complementary solution and 198consistency in model and 464convergence and barrier algorithm 175convergence and numeric inconsistencies 214convergence and performance 207cut callbacks and 464cut callbacks and (example) 464cuts in goals and 438default numeric (example LP) 186feaibility range 192feasibility (Network) 221feasibility and largest bound violation 192feasibility default 192feasibility, reducing 191integralityexample (Java API) 95Markowitz 188Markowitz and numeric difficulty 189Markowitz, increasing to stay feasible 189optimality 192optimality (Network) 221optimality, reducing 191relative objective difference and 266relative optimality 291ILOG CPLEX 11.0 — USER’ S MANUAL 529
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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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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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Page 497 and 498: optimizer turns out to be the faste
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Page 501 and 502: I N D E XIndexAabsolute objective d
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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: eturn statusBounded (Java API) 81Er
Page 531 and 532: arrier 208WorkMem 293WorkMem parame
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