ILOG CPLEX 11.0 User's Manual

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Special Goals in Branch & CutILOG CPLEX provides a selection of special goals that can be used to specify how toproceed in branch & cut. These special goals are:◆ Or Goal on page 430◆ And Goal on page 430◆ Fail Goal on page 430◆ Local Cut Goal on page 431◆ Null Goal on page 431◆ Branch as CPLEX Goal on page 431◆ Solution Goal on page 432Or GoalAn Or goal is a goal that creates subnodes of the current node. This function takes at least 2and up to 6 goals as arguments. For each of its arguments, the Or goal will create a subnodein such a way that when that subnode is processed, the corresponding goal will be executed.After the goal has been executed, the current node is immediately deactivated.In the C++ API, an Or goal is returned by IloCplex::GoalI::OrGoal.In the Java API, an Or goal is returned by the method IloCplex.or.In the .NET API, an Or goal is returned by the method Cplex.Or.And GoalAn And goal also takes goals as arguments. It returns a goal that will cause ILOG CPLEX toexecute all the goals passed as arguments in the order of the arguments.In the C++ API, an And goal is returned by IloCplex::GoalI::AndGoal.In the Java API, an And goal is returned by the method IloCplex.and.In the .NET API, an And goal is returned by the method Cplex.And.Fail GoalA Fail goal creates a goal that causes ILOG CPLEX to prune the current node. In otherwords, it discontinues the search at the node where the goal is executed. ILOG CPLEX willcontinue with another active node from the tree, if one is available.In the C++ API, a Fail goal is returned by IloCplex::GoalI::FailGoal430 ILOG CPLEX 11.0 — USER’ S MANUAL
In the Java API, a Fail goal is returned by the method IloCplex.failGoal.In the .NET API, a Fail goal is returned by the method Cplex.FailGoal.Local Cut GoalA local cut goal adds a local cut to the node where the goal is executed.In the C++ API, the class IloCplex::Goal has constructors that take an instance ofIloRange or an instance of IloRangeArray (IloRange[]) as an argument. When one ofthese constructors is used, a local cut goal is created.To create local cut goals with the Java API, use the method IloCplex.constraintGoalor if more convenient, one of the methods IloCplex.leGoal, IloCplex.geGoal orIloCplex.eqGoal.In the .NET API, use the methods Cplex.ConstraintGoal, Cplex.EqGoal,Cplex.LeGoal, or CplexGeGoal to create a local cut goal.Null GoalThe 0-goal is also known as a null goal or empty goal.In the C++ API, a null goal is an IloCplex::Goal handle object with a null (0)implementation pointer.A null goal can also be returned by the method IloCplex::GoalI::execute.Use a null goal when you want to instruct ILOG CPLEX not to branch any further. Forexample, when CPLEX finds a feasible solution, and you want to accept it without furtherbranching, a null goal is appropriate.For example, the following sample from the C++ API accepts an integer feasible solutionand stops branching.if ( isIntegerFeasible() )return 0;Branch as CPLEX GoalInstead of instructing ILOG CPLEX not to branch any further (as you can do with a nullgoal), it is also possible to tell ILOG CPLEX to branch as it normally would; in other words,to branch as CPLEX. You give ILOG CPLEX this instruction by means of a branching goal.A branching goal tells ILOG CPLEX to take over control of the branch & cut search with itsbuilt-in strategies.ILOG CPLEX 11.0 — USER’ S MANUAL 431
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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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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
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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: How Goals Are Implemented in Branch
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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
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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
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Modifying a ProblemThis section bri
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C H A P T E R33Advanced MIP Control
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value strictly greater than one. It
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Cut CallbackThe next example we con
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problem. Since branching involves a
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C H A P T E R34Parallel OptimizersT
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Threads and Performance Considerati
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3. Call the parallel optimizer with
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optimizer turns out to be the faste
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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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