ILOG CPLEX 11.0 User's Manual

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108 ILOG CPLEX 11.0 — USER’ S MANUAL
C H A P T E R4ILOG CPLEX Callable LibraryThis chapter shows how to write C applications using the ILOG CPLEX Callable Library. Itincludes sections about:◆Architecture of the ILOG CPLEX Callable Library on page 110, including informationabout licensing and about compiling and linking your programs◆ Using the Callable Library in an Application on page 111◆ ILOG CPLEX Programming Practices on page 114◆ Managing Parameters from the Callable Library on page 123◆ Example: Optimizing the Diet Problem in the Callable Library on page 125◆ Using Surplus Arguments for Array Allocations on page 128◆ Example: Using Query Routines lpex7.c on page 130ILOG CPLEX 11.0 — USER’ S MANUAL 109
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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
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 71 and 72: C H A P T E R2ILOG Concert Technolo
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 and 86: IloCplex.setParam(IloCplex.IntParam
Page 87 and 88: Thus, the suggested method for sett
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 97 and 98: C H A P T E R3ILOG Concert Technolo
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: Step 18Enclose the application in t
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
Page 134 and 135: ◆ Test Data on page 135◆ Choose
Page 136 and 137: ◆ Use the MIP optimizer if the pr
Page 138 and 139: Choose Clarity First, Efficiency La
Page 140 and 141: 1. If you can reproduce the behavio
Page 142 and 143: indices, errors will occur. Therefo
Page 144 and 145: Understanding File FormatsThe refer
Page 146 and 147: definitions that it finds. The firs
Page 148 and 149: ◆◆IloXmlReader creates a reader
Page 150 and 151: Controlling Message ChannelsIn both
Page 152 and 153: more calls to the message handling
Page 154 and 155: Concert Technology Message Channels
Page 156 and 157: 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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