ILOG CPLEX 11.0 User's Manual

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choosing in LP (C++ API) 53controlling in IloCplex (C++ API) 55pricing 179type (Java API) 85using multiple 380Algorithm.Barrier (Java API) 89applicationcreating with Concert Technology (C++ API) 45development steps (C++ API) 45arc 218architectureC++ API 44Callable Library (C API) 110Java API 72argumentsnull pointers (C API) 118optional (C API) 118arrayconstructing (Java API) 92creating multi-dimensional (C++ API) 67creating variables in (Java API) 76extensible (C++ API) 49using for I/O (C++ API) 67BBarAlglarge objective values and 216log file and 205settings 213barrier optimizeralgorithm 198algorithms and infeasibility 216barrier display parameter 213centering corrections 213column nonzeros parameter and density 210column nonzeros parameter and instability 214corrections limit 213determinism and 493growth parameter 215infeasibility analysis 216inhibiting dual formulation 176linear 197 to 216log file 202numeric difficulties and 214numerical emphasis and 212parallel 496parallel implementation 493performance tuning 207preprocessing 209primal-dual 174QCP and 242quadratic 227 to 238quadratic constraints and 242row-ordering algorithms 210second-order cone program (SOCP) and 242simplex optimizer and 199solution quality 205solving LP problems 197starting-point heuristics 211threads and 493unbounded optimal face and 215unbounded problems 215uses 199BarStartAlg parameterbarrier starting algorithm 211BAS file format 188basisaccessing information (C++ API) 59advanced, primal feasible 173advanced, role in network 174advanced, starting from (example) 194column generation and 374condition number 187, 191crash parameter and 182crossover algorithms 199current (C API) 111differences between LP and network optimizers 225from basis file 225infeasibility and 190maximum row residuals and 193no factoring in network optimizer 224objective in Phase I and 190optimal, condition number and 187optimal, numeric stability and 187parallel threads and advanced basis 173preprocessing versus starting (MIP) 279previous optimal (C API) 113refactoring rate 182removing objects from (C++ API) 62502 ILOG CPLEX 11.0 — USER’ S MANUAL
ole in converting LP to network flow 226role in converting network-flow to LP 225role in network optimizer 221role in network optimizer to solve QP 222saving best so far 188sensitivity analysis and (Java API) 89singularities and 188starting from advanced 475unexplored nodes in tree (MIP) 293unstable optimal 191BBInterval parametercontrolling branch and cut 267Best Bound 268Best Estimate 268bibliography 39column generation 374Big M 355BndStrenInd parameterMIP preprocessing 278bound violation (LP) 192Bounded return status (Java API) 81branch & cut algorithmcontrolling process with presolve 475heuristic callback in 486Java API 84memory problems and 292MIP performance tuning and 265parameters 267priority order 280solves MIP models 261special ordered sets (SOS) 329storing tree nodes 270, 293tree 265tree and presolvenodesolving linear problem at 474branch variable selection callback 488branching direction (Java API) 87branching goal 431BrDir parametercontrolling branch and cut 267breakpointdiscontinuous piecewise linear and 340example 338piecewise linear function and 338BtTol parameterCbacktrack parameter purpose 268controlling branch and cut 267call by value (C API) 114Callable Librarycategories of routines 110core 110debugging and 139description 28parameters 123tuning tool 166using 109 to 130callbackbranch variable selection 488control (definition) 451control in parallel 453cut 487diagnostic (definition) 449graphic user interface and 462heuristic 485incumbent 489incumbent as filter in solution pool 328informational (definition) 446node selection 489opportunistic parallel mode and 446query (definition) 449resetting to null (C API) 125resetting to null (C++ API) 56solve 490using status variables 461changingbounds setLB (Java API) 94bounds setUB (Java API) 94limit on barrier corrections 213maximization to minimization 224model setLinearCoef (Java API) 94pricing algorithm 221problem typenetwork to LP 225qp 234zeroed_qp 234quadratic coefficients 233type of variable 379ILOG CPLEX 11.0 — USER’ S MANUAL 503
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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
Page 451 and 452: ● Cplex.DisjunctiveCutCallback in
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Page 457 and 458: IloCplex::Callback mycallback = cpl
Page 459 and 460: Here is the previous sample of code
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Page 467 and 468: Interaction Between Callbacks and I
Page 469 and 470: C H A P T E R31Goals and Callbacks:
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Page 473 and 474: C H A P T E R32Advanced Presolve Ro
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
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Page 491 and 492: C H A P T E R34Parallel OptimizersT
Page 493 and 494: Threads and Performance Considerati
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Page 497 and 498: optimizer turns out to be the faste
Page 499 and 500: 0 0 3383.7784 16 4505.0000 Cuts: 35
Page 501: I N D E XIndexAabsolute objective d
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 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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