ILOG CPLEX 11.0 User's Manual

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Example: Using Query Routines lpex7.cThis example uses the ILOG CPLEX Callable Library query routine CPXgetcolname to getthe column names from a problem object. To do so, it applies the programming pattern justoutlined in Using Surplus Arguments for Array Allocations on page 128. It derives from theexample lpex2.c from the ILOG CPLEX Getting Started manual. This query-routineexample differs from that simpler example in several ways:◆The example calls CPXgetcolname twice after optimization: the first call discovers howmuch space to allocate to hold the names; the second call gets the names and stores themin the arrays cur_colname and cur_colnamestore.◆ When the example prints its answer, it uses the names as stored in cur_colname. If nonames exist there, the example creates generic names.This example assumes that the current problem has been read from a file byCPXreadcopyprob. You can adapt the example to use other ILOG CPLEX query routinesto get information about any problem read from a file.The complete program lpex7.c appears online in the standard distribution atyourCPLEXinstallation/examples/src.130 ILOG CPLEX 11.0 — USER’ S MANUAL
Part IIProgramming ConsiderationsThis part of the manual documents concepts that are valid as you develop an application,regardless of the programming language that you choose. It highlights software engineeringconcepts implemented in ILOG CPLEX, concepts that will enable you to develop effectiveapplications to exploit it efficiently. This part contains:◆ Developing CPLEX Applications on page 133◆ Managing Input and Output on page 143◆ Licensing an Application on page 155◆ Tuning Tool on page 161
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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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C H A P T E R2ILOG Concert Technolo
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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
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 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: program—can discover the length o
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
Page 158 and 159: char *inststr = NULL;char *envstr =
Page 160 and 161: The registerLicense Method for Java
Page 162 and 163: not correct that problem either. In
Page 164 and 165: In the Interactive Optimizer, you c
Page 166 and 167: For models not in the current worki
Page 168 and 169: ◆◆In the .NET API, pass an inst
Page 171 and 172: C H A P T E R9Solving LPs: Simplex
Page 173 and 174: The symbolic names for these settin
Page 175 and 176: When no candidates are present, the
Page 177 and 178: solution; but examination of soluti
Page 179 and 180: 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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