ILOG CPLEX 11.0 User's Manual

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Users of the advanced MIP control interface can work with the variables of the presolvedproblem or, by following a few simple rules, can instead work with the variables of theoriginal problem.Tip: The advanced MIP control interface relies heavily on the advanced presolvecapabilities. We suggest that the reader become familiar with Advanced Presolve Routineson page 473, before reading this chapter.Control callbacks in the ILOG Concert Technology CPLEX Library use the variables of theoriginal model. These callbacks are fully documented in the ILOG CPLEX ReferenceManual.Introduction to MIP Control CallbacksAs the reader is no doubt familiar, the process of solving a mixed integer programmingproblem involves exploring a tree of linear programming relaxations. CPLEX repeatedlyselects a node from the tree, solves the LP relaxation at that node, attempts to generatecutting planes to cut off the current solution, invokes a heuristic to try to find an integerfeasible solution “close” to the current relaxation solution, selects a branching variable (aninteger variable whose value in the current relaxation is fractional), and finally places thetwo nodes that result from branching up or down on the branching variable back into thetree.The CPLEX Mixed Integer Optimizer includes methods for each of those important steps(node selection, cutting planes, heuristic, branch variable selection, incumbent replacement).While default CPLEX methods are generally effective, and parameters are available tochoose alternatives if the defaults are not working for a particular problem, there are rarecases where a user may wish to influence or even override CPLEX methods. CPLEXprovides a control callback mechanism to allow the user to do this. If the user installs acontrol callback routine, CPLEX calls this routine during the branch & cut process to allowthe user to intervene.Thread Safety and MIP Control CallbacksWhen you use a control callback with parallel MIP, you must observe several points aboutdefault parameter settings, thread-safety, and parallelism. This section addresses thosepoints.The presence of a control callback in an application with default parameter settings turns offparallel MIP. (Informational callbacks do not have this effect of turning off parallel MIPoptimization by default. For more about informational callbacks, see InformationalCallbacks on page 446.) In order to continue using parallelMIP in the presence of a controlcallback, the user must set the threads parameter (Threads, CPX_PARAM_THREADS) to a484 ILOG CPLEX 11.0 — USER’ S MANUAL
value strictly greater than one. It is then the responsability of the user to implement thatcontrol callback in such a way that the callback is thread-safe. In particular, the controlcallback must be written in such a way that it does not depend on the order in whichcallbacks are called, as no fixed order of calling the callbacks can be guaranteed by parallelILOG CPLEX.With respect to determinism, if the user sets the parallel mode parameter (ParallelMode,CPX_PARAM_PARALLELMODE) to 1 (one) thus invoking deterministic parallel MIPoptimization, it is up to the user to make sure that the control callback does not interfere withthe search in any way that would make the search opportunistic. To make sure of thatcondition, the control callback must use only information queried from ILOG CPLEX itselfwithin the callback as the basis for algorithmic decisions. In other words, no information thataccumulated in an external data structure over several invokations of the control callback canbe used.Presolve and MIP Control CallbacksTis section addresses an important issue related to presolve that the user of MIP controlcallbacks should be aware of.Most of the decisions made within MIP relate to the variables of the problem. The heuristic,for example, finds values for all the variables in the problem that produce a feasible solution.Similarly, branching chooses a variable on which to branch. When considering usercallbacks, the difficulty that arises is that the user is familiar with the variables in the originalproblem, while the branch & cut process is performed on the presolved problem. Many ofthe variables in the original problem may have been modified or removed by presolve.CPLEX provides two options for handling the problem of mapping from the originalproblem to the presolved problem. First, the user may work directly with the presolvedproblem and presolved solution vectors. This is the default. While this option may at firstappear unwieldy, note that the Advanced Presolve Interface allows the user to map betweenoriginal variables and presolved variables. The downside to this option is that the user has tomanually invoke these advanced presolve routines. The second option is to setCPX_PARAM_MIPCBREDLP to CPX_OFF (0), thus requesting that the callback routines workexclusively with original variables. CPLEX automatically translates the data betweenoriginal and presolved data. While the second option is simpler, the first provides morecontrol. These two options will be revisited at several points in this chapter.Heuristic CallbackThe first user callback we consider is the heuristic callback. The first step in using thiscallback is to call CPXsetheuristiccallbackfunc, with a pointer to a callback functionand optionally a pointer to user private data as arguments. We refer the reader to advancedexample admipex2.c for further details of how this callback is used. Once this routine hasILOG CPLEX 11.0 — USER’ S MANUAL 485
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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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Page 435 and 436: The Goal StackTo understand how goa
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Page 445 and 446: C H A P T E R30Using Optimization C
Page 447 and 448: Reference Documents about Informati
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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 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: C H A P T E R33Advanced MIP Control
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
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 and 504: ole in converting LP to network flo
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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