ILOG CPLEX 11.0 User's Manual

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Table 2.4 Algorithm Types for NodeAlgalg Algorithm Type MILP? MIQP? MIQCP?3 IloCplex.Algorithm.Network yes not available not available4 IloCplex.Algorithm.Barrier yes yes yes5 IloCplex.Algorithm.Sifting yes not available not availableControlling ILOG CPLEX OptimizersThough ILOG CPLEX defaults will prove sufficient to solve most problems, ILOG CPLEXoffers a variety of other parameters to control various algorithmic choices. ILOG CPLEXparameters can take values of type boolean, int, double, and string. The parametersare accessed via parameter names defined in classes IloCplex.BooleanParam,IloCplex.IntParam, IloCplex.DoubleParam, and IloCplex.StringParamcorresponding to the parameter type.ParametersParameters are manipulated by means of IloCplex.setParam. For example:cplex.setParam(IloCplex.BooleanParam.PreInd, false);sets the Boolean parameter PreInd to false, instructing ILOG CPLEX not to applypresolve before solving the problem.Integer parameters often indicate a choice from a numbered list of possibilities, rather than aquantity. For example, the class IloCplex.PrimalPricing defines constants with theinteger parameters shown in Table 2.5, Constants in IloCplex.PrimalPricing for bettermaintainability of the code.Table 2.5 Constants in IloCplex.PrimalPricingInteger ParameterConstant in class IloCplex.PrimalPricing0 IloCplex.PrimalPricing.Auto1 IloCplex.PrimalPricing.Devex2 IloCplex.PrimalPricing.Steep3 IloCplex.PrimalPricing.SteepQStart4 IloCplex.PrimalPricing.Full86 ILOG CPLEX 11.0 — USER’ S MANUAL
Thus, the suggested method for setting steepest-edge pricing for use with the primal simplexalgorithm looks like this:cplex.setParam(IloCplex.IntParam.PPriInd,IloCplex.PrimalPricing.Steep);Table 2.6 gives an overview of the classes defining constants for parameters.Table 2.6 Classes with Parameters Defined by Integers.classIloCplex.AlgorithmIloCplex.MIPEmphasisIloCplex.VariableSelectIloCplex.NodeSelectIloCplex.DualPricingIloCplex.PrimalPricingfor use with parameters:IloCplex.IntParam.RootAlgIloCplex.IntParam.NodeAlgIloCplex.IntParam.MIPEmphasisIloCplex.IntParam.VarSelIloCplex.IntParam.NodeSelIloCplex.IntParam.DPriIndIloCplex.IntParam.PPriIndParameters can be queried with method IloCplex.getParam and reset to their defaultsettings with method IloCplex.setDefaults. The minimum and maximum value towhich an integer or double parameter can be set is queried with methodsIloCplex.getMin and IloCplex.getMax, respectively. The default value of a parameteris obtained with IloCplex.getDefault.Priority Orders and Branching DirectionsWhen CPLEX is solving a MIP, another important way for you to control the solutionprocess is by providing priority orders and branching directions for variables. The methodsfor doing so are:●●●IloCplex.setDirection,IloCplex.setDirections,IloCplex.setPriority, and● IloCplex.setPriorities.Priority orders and branch directions allow you to control the branching performed duringbranch & cut in a static way.Dynamic control of the solution process of MIPs is provided through goals or controlcallbacks. Goals are discussed for C++ in Using Goals on page 427. Control callbacks arediscussed in Using Optimization Callbacks on page 445. (Java goals and callbacks areILOG CPLEX 11.0 — USER’ S MANUAL 87
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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
Page 35: Table 1ExamplesExample Source File
Page 38 and 39: ◆◆◆◆◆◆Overview of the A
Page 40 and 41: Wolsey, Laurence A., Integer Progra
Page 43 and 44: C H A P T E R1ILOG Concert Technolo
Page 45 and 46: Compiling and LinkingCompilation an
Page 47 and 48: The class IloNumVar provides method
Page 49 and 50: IloModel object itself and added to
Page 51 and 52: Table 1.1 Concert Technology Modeli
Page 53 and 54: Choosing an OptimizerSolving the ex
Page 55 and 56: Table 1.4 on page 55 summarizes the
Page 57 and 58: Dynamic control of the solution pro
Page 59 and 60: However, querying solution values v
Page 61 and 62: parameter for the getQuality method
Page 63 and 64: model2.add(model1);model2.add(IloCo
Page 65 and 66: Example: Optimizing the Diet Proble
Page 67 and 68: Concert Technology, as demonstrated
Page 69 and 70: method IloModel::add returns the mo
Page 73 and 74: Licenses in a Java ApplicationILOG
Page 75 and 76: Models that consist only of such co
Page 77 and 78: The special case of linear expressi
Page 79 and 80: IloObjective obj = cplex.add(cplex.
Page 81 and 82: Solving the ModelOnce you have crea
Page 83 and 84: IloCplex cplex = new IloCplex();Ilo
Page 85: IloCplex.setParam(IloCplex.IntParam
Page 89 and 90: Basis InformationWhen solving an LP
Page 91 and 92: where A is a sparse matrix. A spars
Page 93 and 94: The example starts by evaluating th
Page 95 and 96: IloMPModeler.setLinearCoefs, and Il
Page 99 and 100: ◆What is the purpose (the objecti
Page 101 and 102: Step 3Create the modelGo to the com
Page 103 and 104: Step 9Add nutritional constraintsGo
Page 105 and 106: Step 14Display the solutionGo to th
Page 107 and 108: Step 18Enclose the application in t
Page 109 and 110: C H A P T E R4ILOG CPLEX Callable L
Page 111 and 112: ◆◆file reading and writing rout
Page 113 and 114: ◆◆If data already exist in MPS,
Page 115 and 116: For example, let’s look at the sy
Page 117 and 118: As you modify a problem object thro
Page 119 and 120: However, ILOG CPLEX updates the nam
Page 121 and 122: whether an optimization should be a
Page 123 and 124: Callable Library have names greater
Page 125 and 126: ◆CPXsetstrparam accepts arguments
Page 127 and 128: Here’s another way to visualize a
Page 129 and 130: program—can discover the length o
Page 131: Part IIProgramming ConsiderationsTh
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◆ Use the MIP optimizer if the pr
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Choose Clarity First, Efficiency La
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1. If you can reproduce the behavio
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indices, errors will occur. Therefo
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Understanding File FormatsThe refer
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definitions that it finds. The firs
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◆◆IloXmlReader creates a reader
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Controlling Message ChannelsIn both
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more calls to the message handling
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Concert Technology Message Channels
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Types of ILM Runtime LicensesILM ru
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char *inststr = NULL;char *envstr =
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The registerLicense Method for Java
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not correct that problem either. In
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In the Interactive Optimizer, you c
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For models not in the current worki
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◆◆In the .NET API, pass an inst
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C H A P T E R9Solving LPs: Simplex
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The symbolic names for these settin
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When no candidates are present, the
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solution; but examination of soluti
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Then to later read an advanced basi
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Table 9.5 PPriInd Parameter Setting
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ILOG CPLEX ignores the coefficients
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Numeric DifficultiesILOG CPLEX is d
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esult is the same as would be obtai
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automatically perturbs the variable
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4. Consider alternate scalings.You
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smaller in absolute value than the
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exceptions are caught as well, and
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C H A P T E R10Solving LPs: Barrier
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The ILOG CPLEX Barrier Optimizer is
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And then you call the solution rout
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Here is an example of a log file fo
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If solution values are large in abs
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Normalized errors, for example, rep
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◆ workmem in the Interactive Opti
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est order. It may require more time
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choice of starting-point heuristic
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To see the current value of the col
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C H A P T E R11Solving Network-Flow
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◆◆l a , the lower bound, sets t
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of the objective function calculate
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then ILOG CPLEX will carry out such
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-a 1 + a 2 - a 8 - a 9 + a 14 = 0-
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C H A P T E R12Solving Problems wit
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convex QPs, Q must be positive semi
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A similar Java program using Concer
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◆ qp indicates that you want ILOG
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RootAlg parameter (QPMETHOD in the
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ilolpex1.cpp counterpart. Here the
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C H A P T E R13Solving Problems wit
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Figure 13.2y(0, 1)(-1, 0)d(1, 0)xc(
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Detecting Problem TypeILOG CPLEX de
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COLUMNSMARK0000 'MARKER''INTORG'C15
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C180 R100 159C180 R119 200C180 R120
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QMATRIXC158 C158 1C158 C189 0.5C189
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◆From the Callable Library, use t
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Part IVDiscrete OptimizationThis pa
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Stating a MIP ProblemA mixed intege
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sections does not matter. To enter
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◆milp, miqp, or miqcpindicating t
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1. finding a succession of improvin
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easonable amount of computation tim
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If the solution to the relaxation s
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anches taken so far in this dive. S
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directives about the order in which
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◆ Parameters Affecting Cuts on pa
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◆●●IloCplex.getNcuts in the J
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Relaxation Induced Neighborhood Sea
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Table 14.10Parameters for Controlli
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Library). If this process succeeds,
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After you have solved a MIP, you wi
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Table 14.13Settings of the MIP Disp
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ILOG CPLEX also logs its addition o
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◆ Parent specifies the NodeID of
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parameter settings are also worth c
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When you set a MIP cutoff value, IL
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memory. It also includes several op
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◆◆●●at problem modification
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◆In the Callable Library, use the
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●●Then call CPXprimopt to optim
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◆ The Incumbent and the Solution
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c17: x1 - y11 >= 0c18: x1 - y21 >=
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Populating the Solution PoolILOG CP
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NodesCuts/Node Left Objective IInf
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Note: The parameter to limit the nu
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Example: Using Populate after MIP O
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Limitations Due to Numeric Difficul
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Examining the Solution PoolIn the I
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For a sample of these methods or ro
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◆◆In the Callable Library (C AP
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Then display the objective value of
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◆ If you want to filter solutions
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●●●In the C++ API, use the me
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NAME locationRNGFILTER f2 -inf 0tra
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continuous, a model containing one
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◆The routine setPriorities sets t
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What Are Semi-Continuous Variables?
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With that model, now the applicatio
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Piecewise Linearity in ILOG CPLEXSo
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Reminder: It may help you understan
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overlaps with an endpoint of two ot
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Figure 18.4400003000020000100000Fig
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in demand; in terms of this model,
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348 ILOG CPLEX 11.0 — USER’ S M
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What Are Logical Constraints?For IL
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◆◆Cplex.NotCplex.IfThenAgain, t
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In Concert Technology applications,
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Describing the ProblemThe problem i
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Notice that how much to use and buy
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Then use a for-loop to add the cons
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These lines (the action of the if-s
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Describing the ProblemThe problem i
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Stating Precedence ConstraintsIn ea
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Solving the ProblemAn emphasis on f
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What Is Column Generation?In colloq
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Solving this model with all columns
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Likewise, the application adds an a
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Pattern Generator ModelThe submodel
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Complete ProgramYou can see the ent
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your problem formulation caused thi
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To control the types of reductions
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What Is Unboundedness?Any class of
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Callable Library use the advanced r
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the conflict to arrive at a minimal
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the IIS finder can, and often more.
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Then you will see results like thes
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Now view the entire conflict with t
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The constraints in conflict with th
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If a model contains more than one c
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Immediately after that statement, i
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◆ Groups in the Conflict Refiner
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The infeasibility on which FeasOpt
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Now the following lines invoke Feas
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suggests decreasing the upper bound
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C H A P T E R28User-Cut and Lazy-Co
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However, there is an important dist
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◆◆Through the routine CPXreadco
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Here is an example of an MPS file e
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C H A P T E R29Using GoalsThis chap
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How Goals Are Implemented in Branch
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In the Java API, a Fail goal is ret
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This return statement returns an An
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The Goal StackTo understand how goa
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soon as they are enclosed in a goal
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The global cut goal for lhs[i] ≤
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If a node has multiple evaluators a
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As this example is an extension of
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C H A P T E R30Using Optimization C
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Reference Documents about Informati
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Query or Diagnostic CallbacksQuery
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● Cplex.DisjunctiveCutCallback in
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◆◆●●Cplex.HeuristicCallback
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It is not customary to write such a
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IloCplex::Callback mycallback = cpl
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Here is the previous sample of code
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◆◆cbdata, a pointer to ILOG CPL
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conventions of the Interactive Opti
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een added, it can be deleted by cal
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Interaction Between Callbacks and I
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C H A P T E R31Goals and Callbacks:
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The only functionality that is not
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C H A P T E R32Advanced Presolve Ro
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once. All of the node relaxation so
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- 5x1, + x2 ≤ 0 2y1, + y2, ≥ 1x
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problem, the problem has a presolve
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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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