ILOG CPLEX 11.0 User's Manual

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C H A P T E R1ILOG Concert Technology for C++ UsersThis chapter shows how to write C++ programs using ILOG CPLEX Concert Technologyfor C++ users. It includes sections about:◆Architecture of a CPLEX C++ Application on page 44, including information aboutlicensing, compiling, and linking your programs◆ Creating a C++ Application with Concert Technology on page 45◆ Modeling an Optimization Problem with Concert Technology on page 45◆ Solving the Model on page 50◆ Accessing Solution Information on page 57◆ Modifying a Model on page 61◆ Handling Errors on page 63◆ Example: Optimizing the Diet Problem in C++ on page 65ILOG CPLEX 11.0 — USER’ S MANUAL 43
Page 1 and 2: ILOG CPLEX 11.0User’s ManualSepte
Page 3 and 4: C O N T E N T STable of ContentsILO
Page 5 and 6: Building the Model by Column . . .
Page 8: Program Description . . . . . . . .
Page 13 and 14: Cholesky Factor in the Log File . .
Page 15: Examples: QCP . . . . . . . . . . .
Page 19 and 20: Chapter 18 Using Piecewise Linear F
Page 21 and 22: Representing the Data. . . . . . .
Page 23 and 24: Reading and Writing MPS Files . . .
Page 25 and 26: Protected Variables in Presolve Red
Page 27 and 28: P R E F A C EMeet ILOG CPLEXILOG CP
Page 29 and 30: When a linear optimization problem
Page 31 and 32: ◆◆◆◆implemented in ILOG CPL
Page 33 and 34: Solution Pool: Generating and Keepi
Page 35: Table 1ExamplesExample Source File
Page 38 and 39: ◆◆◆◆◆◆Overview of the A
Page 40 and 41: Wolsey, Laurence A., Integer Progra
Page 44 and 45: Architecture of a CPLEX C++ Applica
Page 46 and 47: ● Defining Variables and Expressi
Page 48 and 49: modeling API provided by Concert Te
Page 50 and 51: Solving the ModelILOG CPLEX general
Page 52 and 53: models and algorithm-objects can be
Page 54 and 55: Table 1.2 Optimizer Options in IloC
Page 56 and 57: Some integer parameters are tied to
Page 58 and 59: Table 1.6 Algorithm Status and Info
Page 60 and 61: Unfortunately, there is no simple c
Page 62 and 63: Concert Technology also provides a
Page 64 and 65: Objects of the exception class IloC
Page 66 and 67: your diet. Also, you go through the
Page 68 and 69: data and return an array of modelin
Page 70 and 71: Complete ProgramThe complete progra
Page 72 and 73: Architecture of a CPLEX Java Applic
Page 74 and 75: Modeling an Optimization Problem wi
Page 76 and 77: Using IloModelerIloModeler defines
Page 78 and 79: Objective FunctionsThe objective fu
Page 80 and 81: With what you have learned so far,
Page 82 and 83: For example, an Optimal status indi
Page 84 and 85: ◆A single QP is solved if both Il
Page 86 and 87: Table 2.4 Algorithm Types for NodeA
Page 88 and 89: similar to the C++ goals and callba
Page 90 and 91: One approach, invoked by the method
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objects define how to install a new
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Once the model is created, solving
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96 ILOG CPLEX 11.0 — USER’ S MA
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Note: This chapter consists of a tu
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public class Diet {internal class D
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Step 6Set up rowsGo to the comment
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Step 12Create variablesGo to the co
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Step 16Read the command line (data
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108 ILOG CPLEX 11.0 — USER’ S M
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Architecture of the ILOG CPLEX Call
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This routine checks for a valid ILO
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Destroy the Problem ObjectUse the r
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Data TypesIn the Callable Library,
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Parameter RoutinesYou can set many
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Using Diagnostic Routines for Debug
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and CPXfputs. Callable Library Rout
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shows you the default, the maximum,
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Creating a Model Row by RowYou walk
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After this, the program finishes by
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Example: Using Query Routines lpex7
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C H A P T E R5Developing CPLEX Appl
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◆◆◆If your Callable Library a
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Avoid Side-EffectsIt is good idea t
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Optimizer correspond exactly to rou
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◆ Make Local Variables Temporaril
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C H A P T E R6Managing Input and Ou
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another LP file, the variables ther
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◆ blanks in lieu of repeated name
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Managing Log FilesAs ILOG CPLEX is
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The option value2 lets you specify
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along with a label indicating which
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C H A P T E R7Licensing an Applicat
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API or Cplex in the .NET API) to in
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The Putenv Method for .NET UsersHer
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C H A P T E R8Tuning ToolThe tuning
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Tuning and Time LimitsThe tuning pr
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Fixing Parameters and Tuning Multip
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◆ CPX_TUNE_TILIM specifies that t
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Part IIIContinuous OptimizationThis
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Choosing an Optimizer for Your LP P
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little variability in the righthand
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◆ Preprocessing on page 176◆ St
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espectively, control these two prep
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◆◆overall solution time;number
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Use the scaling parameter (ScaInd,
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If you set the memory emphasis para
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Numerically Sensitive DataThere is
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◆◆In Concert Technology, use th
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depends on the current basis. Conse
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For C++ applications, see Accessing
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compute a set of conflicting constr
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Introducing the Barrier OptimizerTh
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◆◆◆Simplex and barrier optimi
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Using SOL File FormatWhen you use t
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Ordering-Algorithm Time in the Log
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Table 10.6ItemBarrier Solution Qual
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iteration. When you adjust barrier
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These reductions can be applied to
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simplex optimizer. While the barrie
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Difficulties during OptimizationNum
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Also if you know that your problem
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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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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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