ILOG CPLEX 11.0 User's Manual

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ILOG CPLEX also logs its addition of cuts to a model. Here is an example of a node log filefrom a problem where ILOG CPLEX added several cover cuts.MIP Presolve eliminated 0 rows and 1 columns.MIP Presolve modified 12 coefficients.Reduced MIP has 15 rows, 32 columns, and 97 nonzeros.Presolve time = 0.00 sec.NodesNode LeftCuts/Objective IInf Best Integer Best Node ItCnt Gap0 0 2819.3574 7 2819.3574 352881.8340 8 Covers: 4 442881.8340 12 Covers: 3 48* 7 6 0 3089.0000 2904.0815 62 5.99%Cover cuts applied: 30Solution pool: 1 solution savedMIP-Integer optimal solution: Objective = 3.0890000000e+003Solution time = 0.10 sec. Iterations = 192 Nodes = 44ILOG CPLEX also logs the number of clique inequalities in the clique table at the beginningof optimization. Cuts generated at intermediate nodes are not logged individually unless theyhappen to be generated at a node logged for other reasons. ILOG CPLEX logs the number ofapplied cuts of all classes at the end.ILOG CPLEX also indicates, in the node log file, each instance of a successful applicationof the node heuristic. The following example shows a node log file for a problem where theheuristic found a solution at node 0. The + denotes an incumbent generated by the heuristic.NodesCuts/Node Left Objective IInf Best Integer Best Node ItCnt Gap0 0 403.8465 640 403.8465 4037405.2839 609 Cliques: 10 5208405.2891 612 Cliques: 2 5288Heuristic: feasible at 437.000, still lookingHeuristic: feasible at 437.000, still lookingHeuristic complete* 0+ 0 0 436.0000 405.2891 5288 7.04%Periodically, if the MIP display parameter is 2 or greater, ILOG CPLEX records thecumulative time spent since the beginning of the current MIP optimization and the amountof memory used by branch & cut. (Periodically means that time and memory informationappear either every 20 nodes or ten times the MIP display parameter, whichever is greater.)The Interactive Optimizer prints an additional summary line in the log if optimization stopsbefore it is complete. This summary line shows the best MIP bound, that is, the bestobjective value among all the remaining node subproblems. The following example shows286 ILOG CPLEX 11.0 — USER’ S MANUAL
you lines from a node log file where an integer solution has not yet been found, and the bestremaining objective value is 2973.9912281.MIP-Node limit, no integer solution.Current MIP best bound = 2.9739912281e+03 (gap is infinite)Solution time = 0.01 sec. Iterations = 68 Nodes = 7 (7)Stating a MIP Problem on page 256 presents a typical MIP problem. Here is the node logfile for that problem with the default setting of the MIP display parameter:Tried aggregator 1 time.Aggregator did 1 substitutions.Reduced MIP has 2 rows, 3 columns, and 6 nonzeros.Presolve time = 0.00 sec.Clique table:0 GUB, 0 GUBEQ, 0 two-covers, 0 probedImplBd table: 0 boundsRoot relaxation solution time = 0.00 sec.NodesCuts/Node Left Objective IInf Best Integer Best Node ItCnt Gap0 0 125.2083 1 125.2083 3* 0 122.5000 Cuts: 2 4Mixed integer rounding cuts applied: 1Solution pool: 1 solution savedMIP-Integer optimal solution: Objective = 1.2250000000e+002Solution time = 0.02 sec. Iterations = 4 Nodes = 0These additional items appear only in the node log file (not on screen) in conventionalbranch & cut:◆◆Variable records the name of the variable where ILOG CPLEX branched to create thisnode. If the branch was due to a special ordered set, the name listed here will be theright-most variable in the left subset.B indicates the branching direction:●●●●●●●D means the variables was restricted to a lower value;U means the variable was restricted to a higher value;L means the left subset of the special ordered set was restricted to 0 (zero);R means the right subset of the special ordered set was restricted to 0 (zero);A means that constraints were added or more than one variable was restricted;N means that cuts added to the node LP resulted in an integer feasible solution; nobranching was required;NodeID specifies the node identifier.ILOG CPLEX 11.0 — USER’ S MANUAL 287
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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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Page 239 and 240: C H A P T E R13Solving Problems wit
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Page 243 and 244: Detecting Problem TypeILOG CPLEX de
Page 245 and 246: COLUMNSMARK0000 'MARKER''INTORG'C15
Page 247 and 248: C180 R100 159C180 R119 200C180 R120
Page 249 and 250: QMATRIXC158 C158 1C158 C189 0.5C189
Page 251 and 252: ◆From the Callable Library, use t
Page 253: Part IVDiscrete OptimizationThis pa
Page 256 and 257: Stating a MIP ProblemA mixed intege
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Page 262 and 263: 1. finding a succession of improvin
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Page 266 and 267: If the solution to the relaxation s
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Page 270 and 271: directives about the order in which
Page 272 and 273: ◆ Parameters Affecting Cuts on pa
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Page 278 and 279: Table 14.10Parameters for Controlli
Page 280 and 281: Library). If this process succeeds,
Page 282 and 283: After you have solved a MIP, you wi
Page 284 and 285: Table 14.13Settings of the MIP Disp
Page 288 and 289: ◆ Parent specifies the NodeID of
Page 290 and 291: parameter settings are also worth c
Page 292 and 293: When you set a MIP cutoff value, IL
Page 294 and 295: memory. It also includes several op
Page 296 and 297: ◆◆●●at problem modification
Page 298 and 299: ◆In the Callable Library, use the
Page 300 and 301: ●●Then call CPXprimopt to optim
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Page 306 and 307: Populating the Solution PoolILOG CP
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Page 310 and 311: Note: The parameter to limit the nu
Page 312 and 313: Example: Using Populate after MIP O
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Page 316 and 317: Examining the Solution PoolIn the I
Page 318 and 319: For a sample of these methods or ro
Page 320 and 321: ◆◆In the Callable Library (C AP
Page 322 and 323: Then display the objective value of
Page 324 and 325: ◆ If you want to filter solutions
Page 326 and 327: ●●●In the C++ API, use the me
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Page 330 and 331: continuous, a model containing one
Page 332 and 333: ◆The routine setPriorities sets t
Page 334 and 335: 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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