ILOG CPLEX 11.0 User's Manual

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Where to Find Examples of Informational CallbacksFor examples of how to create and use an informational callback, see the following samplesof code in yourCPLEXhome/examples/src.◆◆◆◆◆mipex4.cilomipex4.cppIloMIPex4.javaMIPex4.csMIPex4.vbWhat Informational Callbacks Can ReturnTable 30.1Information Returned by Informational CallbacksSymbolic nameCPX_CALLBACK_INFO_BEST_INTEGERCPX_CALLBACK_INFO_MY_THREAD_NUMCPX_CALLBACK_INFO_BEST_REMAININGCPX_CALLBACK_INFO_NODE_COUNTCPX_CALLBACK_INFO_NODES_LEFTCPX_CALLBACK_INFO_MIP_ITERATIONSCPX_CALLBACK_INFO_CUTOFFCPX_CALLBACK_INFO_MIP_FEASMeaningobjective value of the best integer solutionidentifying number of the thread where the call of thecallback occurredobjective value of the best remaining nodehow many nodes have been processedhow many nodes remain to be processedhow many MIP iterations have occurredcutoff informationfeasibility informationCPX_CALLBACK_INFO_PROBE_PHASE which phase of probing (0-3)CPX_CALLBACK_INFO_PROBE_PROGRESSCPX_CALLBACK_INFO_FRACCUT_PROGRESSCPX_CALLBACK_INFO_DISJCUT_PROGRESSCPX_CALLBACK_INFO_FLOWMIR_PROGRESSprogress in probing expressed as a fractionprogress in fractional cuts expressed as a fractionprogress in disjunctive cuts expressed as a fractionprogress in MIR cuts expressed as a fraction448 ILOG CPLEX 11.0 — USER’ S MANUAL
Query or Diagnostic CallbacksQuery or diagnostic callbacks allow you to monitor an ongoing optimization, and optionallyto abort it (that is, to terminate it). Query callbacks access more detailed information aboutthe current optimization than do informational callbacks. As a side effect, query ordiagnostic callbacks may slow progress. Furthermore, query or diagnostic callbacks makeassumptions about the traversal of a conventional branch & cut tree; those assumptionsabout a mixed integer program (MIP) may be incorrect during during dynamic search orduring deterministic search in parallel optimization. For more detail about this point, seeQuery Callbacks and Dynamic Search on page 451 and Query Callbacks and DeterministicParallel Search on page 451.Where Query Callbacks Are CalledQuery or diagnostic callbacks are distinguished by the place where they are called during anoptimization. There are nine such places where ILOG CPLEX calls a query or diagnosticcallback:◆◆◆◆The presolve query callback is called regularly during presolve.●●●●IloCplex::PresolveCallbackI in the C++ APIIloCplex.PresolveCallback in the Java APICplex.PresolveCallback in the .NET APICPXsetlpcallbackfunc in the Callable Library (C API)The crossover query callback is called regularly during crossover from a barrier solutionto a simplex basis.●●●●IloCplex::CrossoverCallbackI in the C++ APIIloCplex.CrossoverCallback in the Java APICplex.CrossoverCallback in the .NET APICPXsetlpcallbackfunc in the Callable Library (C API)The network query callback is called regularly during the network simplex algorithm.●●●●IloCplex::NetworkCallbackI in the C++ APIIloCplex.NetworkCallback in the Java APICplex.NetworkCallback in the .NET APICPXsetnetcallbackfunc in the Callable Library (C API)The barrier query callback is called at each iteration during the barrier algorithm.ILOG CPLEX 11.0 — USER’ S MANUAL 449
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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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Page 410 and 411: The infeasibility on which FeasOpt
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Page 419 and 420: C H A P T E R28User-Cut and Lazy-Co
Page 421 and 422: However, there is an important dist
Page 423 and 424: ◆◆Through the routine CPXreadco
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Page 427 and 428: C H A P T E R29Using GoalsThis chap
Page 429 and 430: How Goals Are Implemented in Branch
Page 431 and 432: In the Java API, a Fail goal is ret
Page 433 and 434: This return statement returns an An
Page 435 and 436: The Goal StackTo understand how goa
Page 437 and 438: soon as they are enclosed in a goal
Page 439 and 440: The global cut goal for lhs[i] ≤
Page 441 and 442: If a node has multiple evaluators a
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Page 445 and 446: C H A P T E R30Using Optimization C
Page 447: Reference Documents about Informati
Page 451 and 452: ● Cplex.DisjunctiveCutCallback in
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Page 457 and 458: IloCplex::Callback mycallback = cpl
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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
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Page 491 and 492: C H A P T E R34Parallel OptimizersT
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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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