ILOG CPLEX 11.0 User's Manual

More documents

Recommendations

Info

C H A P T E R5Developing CPLEX ApplicationsThis chapter offers suggestions for improving application development and debuggingcompleted applications. It includes information about:◆ Tips for Successful Application Development on page 133◆ Using the Interactive Optimizer for Debugging on page 138◆ Eliminating Common Programming Errors on page 140Tips for Successful Application DevelopmentIn the previous chapters, you saw briefly the minimal steps to use the Component Librariesin an application. This section offers guidelines for successfully developing an applicationthat exploits the ILOG CPLEX Component Libraries according to those steps. Theseguidelines aim to help you minimize development time and maximize applicationperformance.◆ Prototype the Model on page 134◆ Identify Routines to Use on page 134◆ Test Interactively on page 134◆ Assemble Data Efficiently on page 134ILOG CPLEX 11.0 — USER’ S MANUAL 133
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 43 and 44:
C H A P T E R1ILOG Concert Technolo
Page 45 and 46:
Compiling and LinkingCompilation an
Page 47 and 48:
The class IloNumVar provides method
Page 49 and 50:
IloModel object itself and added to
Page 51 and 52:
Table 1.1 Concert Technology Modeli
Page 53 and 54:
Choosing an OptimizerSolving the ex
Page 55 and 56:
Table 1.4 on page 55 summarizes the
Page 57 and 58:
Dynamic control of the solution pro
Page 59 and 60:
However, querying solution values v
Page 61 and 62:
parameter for the getQuality method
Page 63 and 64:
model2.add(model1);model2.add(IloCo
Page 65 and 66:
Example: Optimizing the Diet Proble
Page 67 and 68:
Concert Technology, as demonstrated
Page 69 and 70:
method IloModel::add returns the mo
Page 71 and 72:
C H A P T E R2ILOG Concert Technolo
Page 73 and 74:
Licenses in a Java ApplicationILOG
Page 75 and 76:
Models that consist only of such co
Page 77 and 78:
The special case of linear expressi
Page 79 and 80:
IloObjective obj = cplex.add(cplex.
Page 81 and 82: Solving the ModelOnce you have crea
Page 83 and 84: IloCplex cplex = new IloCplex();Ilo
Page 85 and 86: IloCplex.setParam(IloCplex.IntParam
Page 87 and 88: Thus, the suggested method for sett
Page 89 and 90: Basis InformationWhen solving an LP
Page 91 and 92: where A is a sparse matrix. A spars
Page 93 and 94: The example starts by evaluating th
Page 95 and 96: IloMPModeler.setLinearCoefs, and Il
Page 97 and 98: C H A P T E R3ILOG Concert Technolo
Page 99 and 100: ◆What is the purpose (the objecti
Page 101 and 102: Step 3Create the modelGo to the com
Page 103 and 104: Step 9Add nutritional constraintsGo
Page 105 and 106: Step 14Display the solutionGo to th
Page 107 and 108: Step 18Enclose the application in t
Page 109 and 110: C H A P T E R4ILOG CPLEX Callable L
Page 111 and 112: ◆◆file reading and writing rout
Page 113 and 114: ◆◆If data already exist in MPS,
Page 115 and 116: For example, let’s look at the sy
Page 117 and 118: As you modify a problem object thro
Page 119 and 120: However, ILOG CPLEX updates the nam
Page 121 and 122: whether an optimization should be a
Page 123 and 124: Callable Library have names greater
Page 125 and 126: ◆CPXsetstrparam accepts arguments
Page 127 and 128: Here’s another way to visualize a
Page 129 and 130: program—can discover the length o
Page 131: Part IIProgramming ConsiderationsTh
Page 135 and 136: ◆◆◆If your Callable Library a
Page 137 and 138: Avoid Side-EffectsIt is good idea t
Page 139 and 140: Optimizer correspond exactly to rou
Page 141 and 142: ◆ Make Local Variables Temporaril
Page 143 and 144: C H A P T E R6Managing Input and Ou
Page 145 and 146: another LP file, the variables ther
Page 147 and 148: ◆ blanks in lieu of repeated name
Page 149 and 150: Managing Log FilesAs ILOG CPLEX is
Page 151 and 152: The option value2 lets you specify
Page 153 and 154: along with a label indicating which
Page 155 and 156: C H A P T E R7Licensing an Applicat
Page 157 and 158: API or Cplex in the .NET API) to in
Page 159 and 160: The Putenv Method for .NET UsersHer
Page 161 and 162: C H A P T E R8Tuning ToolThe tuning
Page 163 and 164: Tuning and Time LimitsThe tuning pr
Page 165 and 166: Fixing Parameters and Tuning Multip
Page 167 and 168: ◆ CPX_TUNE_TILIM specifies that t
Page 169: Part IIIContinuous OptimizationThis
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
Page 196 and 197:
196 ILOG CPLEX 11.0 — USER’ S M
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
Page 220 and 221:
Figure 11.1A8 (0, 10, $4)N5+5A7 (10
Page 222 and 223:
You can also use the network optimi
Page 224 and 225:
The function buildNetwork also incl
Page 226 and 227:
LP problem and this command, ILOG C
Page 228 and 229:
Identifying Convex QPsConventionall
Page 230 and 231:
provide as input (Q + Q')/2 instead
Page 232 and 233:
Saving QP ProblemsAfter you enter a
Page 234 and 235:
To change the off-diagonal quadrati
Page 236 and 237:
optimizers are primal-dual in natur
Page 238 and 239:
Like other applications based on th
Page 240 and 241:
subject to Ax ~ band a T i x + x T
Page 242 and 243:
Figure 13.3y(0, 1)(-1, 0)f(1, 0)xe(
Page 244 and 245:
NAMEROWSN R100L R118L R119L R120L R
Page 246 and 247:
C167 R100 183C167 R124 -230C167 R12
Page 248 and 249:
RHSrhs R118 -5rhs R119 2700rhs R120
Page 250 and 251:
◆◆◆lp indicates that you want
Page 252 and 253:
Page 255 and 256:
C H A P T E R14Solving Mixed Intege
Page 257 and 258:
Considering Preliminary IssuesWhen
Page 259 and 260:
Changing Problem Type in MIPsConcer
Page 261 and 262:
solution the value for the variable
Page 263 and 264:
solutions may be discovered along t
Page 265 and 266:
Tuning Tool on page 161 explains mo
Page 267 and 268:
Applying HeuristicsPeriodically dur
Page 269 and 270:
Selecting VariablesAfter a node has
Page 271 and 272:
eginning and yet pay off with short
Page 273 and 274:
Generalized Upper Bound (GUB) Cover
Page 275 and 276:
it allows a generous number of cuts
Page 277 and 278:
●●CPX_PARAM_TILIM in the Callab
Page 279 and 280:
It is possible to apply preprocessi
Page 281 and 282:
should be assigned higher priority
Page 283 and 284:
variables, however, lose feasibilit
Page 285 and 286:
As you can see in that example, ILO
Page 287 and 288:
you lines from a node log file wher
Page 289 and 290:
Too Much Time at Node 0If you obser
Page 291 and 292:
These limits are controlled by the
Page 293 and 294:
As memory gets tight, you may obser
Page 295 and 296:
Callable Library). If that paramete
Page 297 and 298:
Difficulty Solving Subproblems: Ove
Page 299 and 300:
Examples: Optimizing a Simple MIP P
Page 301 and 302:
C H A P T E R15Solution Pool: Gener
Page 303 and 304:
transportation costs incurred to sh
Page 305 and 306:
Filling the Solution PoolThere are
Page 307 and 308:
The amount of preparation in the fi
Page 309 and 310:
immediately invoke populate a secon
Page 311 and 312:
generate solutions of both improvin
Page 313 and 314:
Enumerating All SolutionsWith the s
Page 315 and 316:
improve the solution pool. Changes
Page 317 and 318:
◆◆◆In Concert Technology, the
Page 319 and 320:
◆In the Interactive Optimizer, us
Page 321 and 322:
Parameters of the Solution PoolILOG
Page 323 and 324:
Example: Diverse Solutions through
Page 325 and 326:
existing partial solution. Several
Page 327 and 328:
●●IloCplex.writeFilters in the
Page 329 and 330:
16Using Special Ordered Sets (SOS)I
Page 331 and 332:
Declaring SOS MembersILOG CPLEX off
Page 333 and 334:
C H A P T E R17Using Semi-Continuou
Page 335 and 336:
may be on or off (producing or not
Page 337 and 338:
C H A P T E R18Using Piecewise Line
Page 339 and 340:
functions frequently represent situ
Page 341 and 342:
Figure 18.25f(x)4321x01 2 3 4 5 6 7
Page 343 and 344:
Variable Shipping CostsNow consider
Page 345 and 346:
Developing a ModelAs in other examp
Page 347 and 348:
Solving the ProblemThe following li
Page 349 and 350:
C H A P T E R19Logical Constraints
Page 351 and 352:
◆!= that is, the exclusive-or rel
Page 353 and 354:
Logical Constraints for CountingIn
Page 355 and 356:
20Using Indicator ConstraintsThis c
Page 357 and 358:
What Are Indicator Variables?The bi
Page 359 and 360:
C H A P T E R21Using Logical Constr
Page 361 and 362:
What Is Known?In this particular ex
Page 363 and 364:
●The fact that a limited amount o
Page 365 and 366:
Formulating Logical ConstraintsYou
Page 367 and 368:
C H A P T E R22Early Tardy Scheduli
Page 369 and 370:
Reading the DataThe first part of t
Page 371 and 372:
Consequently, the function can be r
Page 373 and 374:
C H A P T E R23Using Column Generat
Page 375 and 376:
or of other subclasses of IloAlgori
Page 377 and 378:
Developing the Model: Building and
Page 379 and 380:
newPatt[i] for constraint Fill[i];
Page 381 and 382:
The heart of the example is here, i
Page 383 and 384:
Part VInfeasibility and Unboundedne
Page 385 and 386:
24Preprocessing and FeasibilityILOG
Page 387 and 388:
25Managing UnboundednessThis chapte
Page 389 and 390:
Other forms of avoiding under-const
Page 391 and 392:
C H A P T E R26Diagnosing Infeasibi
Page 393 and 394:
How to Invoke the Conflict RefinerT
Page 395 and 396:
This simple model, for example, mig
Page 397 and 398:
In a larger conflict, you can selec
Page 399 and 400:
change bound service lower 2.8and r
Page 401 and 402:
The model is now restored to feasib
Page 403 and 404:
An attempt to display or access a c
Page 405 and 406:
the Conflict Refiner on page 394. Y
Page 407 and 408:
three are combined into a skills co
Page 409 and 410:
C H A P T E R27Repairing Infeasibil
Page 411 and 412:
Specifying PreferencesYou specify t
Page 413 and 414:
The solution values of [40, 30, 80]
Page 415 and 416:
ound; there is no incentive in the
Page 417:
Part VIAdvanced Programming Techniq
Page 420 and 421:
Figure 28.1ycutting planesobjective
Page 422 and 423:
Adding User Cuts and Lazy Constrain
Page 424 and 425:
Reading and Writing SAV FilesUser c
Page 426 and 427:
Page 428 and 429:
Branch & Cut with GoalsGoals allow
Page 430 and 431:
Special Goals in Branch & CutILOG C
Page 432 and 433:
The following lines in the C++ API
Page 434 and 435:
MyBranchGoal and there is no helper
Page 436 and 437:
When ILOG CPLEX processes one of th
Page 438 and 439:
Although it is now solvable directl
Page 440 and 441:
current node. Thus, if the relaxati
Page 442 and 443:
other node. By default, the candida
Page 444 and 445:
follows the branching performed by
Page 446 and 447:
◆ Return Values for Callbacks on
Page 448 and 449:
Where to Find Examples of Informati
Page 450 and 451:
◆◆◆◆◆●●●●IloCplex
Page 452 and 453:
parallelism, if the application is
Page 454 and 455:
Implementing Callbacks in ILOG CPLE
Page 456 and 457:
Table 30.2Callback MacrosCallback M
Page 458 and 459:
Example: Deriving the Simplex Callb
Page 460 and 461:
The complete program, ilolpex4.cpp,
Page 462 and 463:
◆ Before the program calls CPXlpo
Page 464 and 465:
Notice the comma (,) between the ty
Page 466 and 467:
The Java implementation of the call
Page 468 and 469:
Terminating without CallbacksIf you
Page 470 and 471:
◆◆◆●●With goals, you can
Page 472 and 473:
Page 474 and 475:
Introduction to PresolveThis discus
Page 476 and 477:
Restricting Presolve ReductionsAs m
Page 478 and 479:
on. The reasons relate to the fact
Page 480 and 481:
performed on the problem. This func
Page 482 and 483:
Page 484 and 485:
Users of the advanced MIP control i
Page 486 and 487:
een called, CPLEX calls the user ca
Page 488 and 489:
are violated. The user can do this
Page 490 and 491:
Solve CallbackThe final callback we
Page 492 and 493:
parameter (Threads, CPX_PARAM_THREA
Page 494 and 495:
optimization requires less synchron
Page 496 and 497:
the parallel MIP optimizer in deter
Page 498 and 499:
Root Relaxation and Parallel MIP Pr
Page 500 and 501:
If a log file is active when the cl
Page 502 and 503:
choosing in LP (C++ API) 53controll
Page 504 and 505:
variable type (C++ API) 62channel e
Page 506 and 507:
MIP preprocessing 278CPX_PARAM_COVE
Page 508 and 509:
CPX_PREREDUCE_NO_PRIMALORDUAL 477CP
Page 510 and 511:
CPXmCPXmemcpy routine 122CPXMIP_ABO
Page 512 and 513:
deterministic searchbarrier optimiz
Page 514 and 515:
feasibility tolerancedefault 192lar
Page 516 and 517:
Iill-conditionedbasis 193factors in
Page 518 and 519:
eturn status (C++ API) 58return sta
Page 520 and 521:
definition 256definition (Java API)
Page 522 and 523:
primal reductions and 385representi
Page 524 and 525:
Network optimizergeneral observatio
Page 526 and 527:
quadraticconstraints 239convex cons
Page 528 and 529:
IloCplex C++ class 56setExpr method
Page 530 and 531:
elative optimality default 291role
Page 532:
show all

ILOG CPLEX 11.0 User's Manual

Create successful ePaper yourself

Delete template?

Save as template?