ILOG CPLEX 11.0 User's Manual

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C H A P T E R9Solving LPs: Simplex OptimizersThe preceding chapters have focused on the details of writing applications that modeloptimization problems and access the solutions to those problems, with minimal attention tothe optimizer that solves them, because most models are solved well by the defaultoptimizers provided by ILOG CPLEX. For instances where a user wishes to exert moredirect influence over the solution process, ILOG CPLEX provides a number of features thatmay be of interest.This chapter and the next one tell you more about solving linear programs with the LPoptimizers of ILOG CPLEX. This chapter emphasizes primal and dual simplex optimizers.It contains sections about:◆ Choosing an Optimizer for Your LP Problem on page 172◆ Tuning LP Performance on page 175◆ Diagnosing Performance Problems on page 183◆ Diagnosing LP Infeasibility on page 189◆ Examples: Using a Starting Basis in an LP Problem on page 194ILOG CPLEX 11.0 — USER’ S MANUAL 171
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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
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
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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 172 and 173: Choosing an Optimizer for Your LP P
Page 174 and 175: little variability in the righthand
Page 176 and 177: ◆ Preprocessing on page 176◆ St
Page 178 and 179: espectively, control these two prep
Page 180 and 181: ◆◆overall solution time;number
Page 182 and 183: Use the scaling parameter (ScaInd,
Page 184 and 185: If you set the memory emphasis para
Page 186 and 187: Numerically Sensitive DataThere is
Page 188 and 189: ◆◆In Concert Technology, use th
Page 190 and 191: depends on the current basis. Conse
Page 192 and 193: For C++ applications, see Accessing
Page 194 and 195: compute a set of conflicting constr
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Page 198 and 199: Introducing the Barrier OptimizerTh
Page 200 and 201: ◆◆◆Simplex and barrier optimi
Page 202 and 203: Using SOL File FormatWhen you use t
Page 204 and 205: Ordering-Algorithm Time in the Log
Page 206 and 207: Table 10.6ItemBarrier Solution Qual
Page 208 and 209: iteration. When you adjust barrier
Page 210 and 211: These reductions can be applied to
Page 212 and 213: simplex optimizer. While the barrie
Page 214 and 215: Difficulties during OptimizationNum
Page 216 and 217: Also if you know that your problem
Page 218 and 219: Choosing an Optimizer: Network Cons
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Figure 11.1A8 (0, 10, $4)N5+5A7 (10
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You can also use the network optimi
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The function buildNetwork also incl
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LP problem and this command, ILOG C
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Identifying Convex QPsConventionall
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provide as input (Q + Q')/2 instead
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Saving QP ProblemsAfter you enter a
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To change the off-diagonal quadrati
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optimizers are primal-dual in natur
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Like other applications based on th
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subject to Ax ~ band a T i x + x T
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Figure 13.3y(0, 1)(-1, 0)f(1, 0)xe(
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NAMEROWSN R100L R118L R119L R120L R
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C167 R100 183C167 R124 -230C167 R12
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RHSrhs R118 -5rhs R119 2700rhs R120
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◆◆◆lp indicates that you want
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252 ILOG CPLEX 11.0 — USER’ S M
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C H A P T E R14Solving Mixed Intege
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Considering Preliminary IssuesWhen
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Changing Problem Type in MIPsConcer
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solution the value for the variable
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solutions may be discovered along t
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Tuning Tool on page 161 explains mo
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Applying HeuristicsPeriodically dur
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Selecting VariablesAfter a node has
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eginning and yet pay off with short
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Generalized Upper Bound (GUB) Cover
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it allows a generous number of cuts
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●●CPX_PARAM_TILIM in the Callab
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It is possible to apply preprocessi
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should be assigned higher priority
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variables, however, lose feasibilit
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As you can see in that example, ILO
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you lines from a node log file wher
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Too Much Time at Node 0If you obser
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These limits are controlled by the
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As memory gets tight, you may obser
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Callable Library). If that paramete
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Difficulty Solving Subproblems: Ove
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Examples: Optimizing a Simple MIP P
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C H A P T E R15Solution Pool: Gener
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transportation costs incurred to sh
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Filling the Solution PoolThere are
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The amount of preparation in the fi
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immediately invoke populate a secon
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generate solutions of both improvin
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Enumerating All SolutionsWith the s
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improve the solution pool. Changes
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◆◆◆In Concert Technology, the
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◆In the Interactive Optimizer, us
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Parameters of the Solution PoolILOG
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Example: Diverse Solutions through
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existing partial solution. Several
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●●IloCplex.writeFilters in the
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16Using Special Ordered Sets (SOS)I
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Declaring SOS MembersILOG CPLEX off
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C H A P T E R17Using Semi-Continuou
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may be on or off (producing or not
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C H A P T E R18Using Piecewise Line
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functions frequently represent situ
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Figure 18.25f(x)4321x01 2 3 4 5 6 7
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Variable Shipping CostsNow consider
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Developing a ModelAs in other examp
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Solving the ProblemThe following li
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C H A P T E R19Logical Constraints
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◆!= that is, the exclusive-or rel
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Logical Constraints for CountingIn
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20Using Indicator ConstraintsThis c
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What Are Indicator Variables?The bi
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C H A P T E R21Using Logical Constr
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What Is Known?In this particular ex
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●The fact that a limited amount o
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Formulating Logical ConstraintsYou
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C H A P T E R22Early Tardy Scheduli
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Reading the DataThe first part of t
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Consequently, the function can be r
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C H A P T E R23Using Column Generat
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or of other subclasses of IloAlgori
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Developing the Model: Building and
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newPatt[i] for constraint Fill[i];
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The heart of the example is here, i
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Part VInfeasibility and Unboundedne
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24Preprocessing and FeasibilityILOG
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25Managing UnboundednessThis chapte
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Other forms of avoiding under-const
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C H A P T E R26Diagnosing Infeasibi
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How to Invoke the Conflict RefinerT
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This simple model, for example, mig
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In a larger conflict, you can selec
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change bound service lower 2.8and r
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The model is now restored to feasib
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An attempt to display or access a c
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the Conflict Refiner on page 394. Y
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three are combined into a skills co
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C H A P T E R27Repairing Infeasibil
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Specifying PreferencesYou specify t
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The solution values of [40, 30, 80]
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ound; there is no incentive in the
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Part VIAdvanced Programming Techniq
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Figure 28.1ycutting planesobjective
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Adding User Cuts and Lazy Constrain
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Reading and Writing SAV FilesUser c
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Branch & Cut with GoalsGoals allow
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Special Goals in Branch & CutILOG C
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The following lines in the C++ API
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MyBranchGoal and there is no helper
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When ILOG CPLEX processes one of th
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Although it is now solvable directl
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current node. Thus, if the relaxati
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other node. By default, the candida
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follows the branching performed by
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◆ Return Values for Callbacks on
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Where to Find Examples of Informati
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◆◆◆◆◆●●●●IloCplex
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parallelism, if the application is
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Implementing Callbacks in ILOG CPLE
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Table 30.2Callback MacrosCallback M
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Example: Deriving the Simplex Callb
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The complete program, ilolpex4.cpp,
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◆ Before the program calls CPXlpo
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Notice the comma (,) between the ty
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The Java implementation of the call
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Terminating without CallbacksIf you
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◆◆◆●●With goals, you can
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Introduction to PresolveThis discus
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Restricting Presolve ReductionsAs m
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on. The reasons relate to the fact
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performed on the problem. This func
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Users of the advanced MIP control i
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een called, CPLEX calls the user ca
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are violated. The user can do this
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Solve CallbackThe final callback we
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parameter (Threads, CPX_PARAM_THREA
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optimization requires less synchron
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the parallel MIP optimizer in deter
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Root Relaxation and Parallel MIP Pr
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If a log file is active when the cl
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choosing in LP (C++ API) 53controll
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variable type (C++ API) 62channel e
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MIP preprocessing 278CPX_PARAM_COVE
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CPX_PREREDUCE_NO_PRIMALORDUAL 477CP
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CPXmCPXmemcpy routine 122CPXMIP_ABO
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deterministic searchbarrier optimiz
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feasibility tolerancedefault 192lar
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Iill-conditionedbasis 193factors in
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eturn status (C++ API) 58return sta
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definition 256definition (Java API)
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primal reductions and 385representi
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Network optimizergeneral observatio
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quadraticconstraints 239convex cons
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IloCplex C++ class 56setExpr method
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elative optimality default 291role
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