ILOG CPLEX 11.0 User's Manual

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◆◆overall solution time;number of Phase I iterations (that is, iterations before ILOG CPLEX arrives at an initialfeasible solution);◆ total number of iterations.ILOG CPLEX records those values in the log file as it works. (By default, ILOG CPLEXcreates the log file in the directory where it is executing, and it names the log filecplex.log. Managing Log Files on page 149 tells you how to rename and relocate this logfile.)If the number of iterations required to solve your problem is approximately the same as thenumber of rows, then you are doing well. If the number of iterations is three times greaterthan the number of rows (or more), then it may very well be possible to improveperformance by changing the parameter that sets the pricing algorithm, DPriInd for thedual simplex optimizer or PPriInd for the primal simplex optimizer.The symbolic names for the acceptable values for these parameters appear in Table 9.4 andTable 9.5. The default value in both cases is 0 (zero).Table 9.4 DPriInd Parameter Settings for Dual Simplex Pricing AlgorithmDescription Concert Callable Library0 set automatically DPriIndAuto CPX_DPRIIND_AUTO1 standard dual pricing DPriIndFull CPX_DPRIIND_FULL2 steepest-edge pricing DPriIndSteep CPX_DPRIIND_STEEP3 steepest-edge in slack space DPriIndFullSteep CPX_DPRIIND_FULLSTEEP4 steepest-edge, unit initial norms DPriIndSteepQStart CPX_DPRIIND_STEEPQSTART5 devex pricing DPriIndDevex CPX_DPRIIND_DEVEX180 ILOG CPLEX 11.0 — USER’ S MANUAL
Table 9.5 PPriInd Parameter Settings for Primal Simplex Pricing AlgorithmDescription Concert Callable Library-1 reduced-cost pricing PPriIndPartial CPX_PPRIIND_PARTIAL0 hybrid reduced-cost and devex PPriIndAuto CPX_PPRIIND_AUTO1 devex pricing PPriIndDevex CPX_PPRIIND_DEVEX2 steepest-edge pricing PPriIndSteep CPX_PPRIIND_STEEP3 steepest-edge, slack initial norms PPriIndSteepQStart CPX_PPRIIND_STEEPQSTART4 full pricing PriIndFull CPX_PPRIIND_FULLFor the dual simplex pricing parameter, the default value selects steepest-edge pricing. Thatis, the default (0 or CPX_DPRIIND_AUTO) automatically selects 2 or CPX_DPRIIND_STEEP.For the primal simplex pricing parameter, reduced-cost pricing (-1) is less computationallyexpensive, so you may prefer it for small or relatively easy problems. Try reduced-costpricing, and watch for faster solution times. Also if your problem is dense (say, 20-30nonzeros per column), reduced-cost pricing may be advantageous.In contrast, if you have a more difficult problem taking many iterations to complete Phase Iand arrive at an initial solution, then you should consider devex pricing (1). Devex pricingbenefits more from ILOG CPLEX linear algebra enhancements than does partial pricing, soit may be an attractive alternative to partial pricing in some problems. However, if yourproblem has many columns and relatively few rows, devex pricing is not likely to help much.In such a case, the number of calculations required per iteration will usually bedisadvantageous.If you observe that devex pricing helps, then you might also consider steepest-edgepricing (2). Steepest-edge pricing is computationally more expensive than reduced-costpricing, but it may produce the best results on difficult problems. One way of reducing thecomputational intensity of steepest-edge pricing is to choose steepest-edge pricing withinitial slack norms (3).ScalingPoorly conditioned problems (that is, problems in which even minor changes in data result inmajor changes in solutions) may benefit from an alternative scaling method. Scalingattempts to rectify poorly conditioned problems by multiplying rows or columns byconstants without changing the fundamental sense of the problem. If you observe that yourproblem has difficulty staying feasible during its solution, then you should consider analternative scaling method.ILOG CPLEX 11.0 — USER’ S MANUAL 181
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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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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
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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: Then to later read an advanced basi
Page 183 and 184: ILOG CPLEX ignores the coefficients
Page 185 and 186: Numeric DifficultiesILOG CPLEX is d
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Page 189 and 190: automatically perturbs the variable
Page 191 and 192: 4. Consider alternate scalings.You
Page 193 and 194: smaller in absolute value than the
Page 195 and 196: exceptions are caught as well, and
Page 197 and 198: C H A P T E R10Solving LPs: Barrier
Page 199 and 200: The ILOG CPLEX Barrier Optimizer is
Page 201 and 202: And then you call the solution rout
Page 203 and 204: Here is an example of a log file fo
Page 205 and 206: If solution values are large in abs
Page 207 and 208: Normalized errors, for example, rep
Page 209 and 210: ◆ workmem in the Interactive Opti
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Page 213 and 214: choice of starting-point heuristic
Page 215 and 216: To see the current value of the col
Page 217 and 218: C H A P T E R11Solving Network-Flow
Page 219 and 220: ◆◆l a , the lower bound, sets t
Page 221 and 222: of the objective function calculate
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Page 225 and 226: -a 1 + a 2 - a 8 - a 9 + a 14 = 0-
Page 227 and 228: C H A P T E R12Solving Problems wit
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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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