GAMS â The Solver Manuals - Available Software

More documents

Recommendations

Info

332 MILES Table 8 Transport Model in GAMS/MCP (page 2 of 2) POSITIVE VARIABLES W(I) shadow price at supply node i, P(J) shadow price at demand node j, X(I,J) shipment quantities in cases; EQUATIONS SUPPLY(I) supply limit at plant i, FXDEMAND(J) fixed demand at market j, PRDEMAND(J) price-responsive demand at market j, PROFIT(I,J) zero profit conditions; PROFIT(I,J).. W(I) + C(I,J) =G= P(J); SUPPLY(I).. A(I) =G= SUM(J, X(I,J)); FXDEMAND(J).. SUM(I, X(I,J)) =G= B(J); PRDEMAND(J).. SUM(I, X(I,J)) =G= B(J) * (PBAR(J)/P(J))**ESUB(J); * Declare models including specification of equation-variable association: MODEL FIXEDQTY / PROFIT.X, SUPPLY.W, FXDEMAND.P/ ; MODEL EQUILQTY / PROFIT.X, SUPPLY.W, PRDEMAND.P/ ; * Initial estimate: P.L(J) = 1; W.L(I) = 1; PARAMETER REPORT(*,*,*) Summary report; SOLVE FIXEDQTY USING MCP; REPORT("FIXED",I,J) = X.L(I,J); REPORT("FIXED","Price",J) = P.L(J); REPORT("FIXED",I,"Price") = W.L(I); * Calibrate the demand functions: PBAR(J) = P.L(J); * Replicate the fixed demand equilibrium: SOLVE EQUILQTY USING MCP; REPORT("EQUIL",I,J) = X.L(I,J); REPORT("EQUIL","Price",J) = P.L(J); REPORT("EQUIL",I,"Price") = W.L(I); DISPLAY "BENCHMARK CALIBRATION", REPORT; * Compute a counter-factual equilibrium: C("SEATTLE","CHICAGO") = 0.5 * C("SEATTLE","CHICAGO"); SOLVE FIXEDQTY USING MCP; REPORT("FIXED",I,J) = X.L(I,J); REPORT("FIXED","Price",J) = P.L(J); REPORT("FIXED",I,"Price") = W.L(I); * Replicate the fixed demand equilibrium: SOLVE EQUILQTY USING MCP; REPORT("EQUIL",I,J) = X.L(I,J); REPORT("EQUIL","Price",J) = P.L(J); REPORT("EQUIL",I,"Price") = W.L(I); DISPLAY "Reduced Seattle-Chicago transport cost:", REPORT;
MINOS Bruce A. Murtagh; Graduate School of Management, Macquarie University, Sydney, Australia Michael A. Saunders, Walter Murray; Department of EESOR, Stanford University, CA Philip E. Gill; Department of Mathematics, University of California, San Diego, La Jolla, CA Ramesh Raman, Erwin Kalvelagen; GAMS Development Corporation, Washington D.C. Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 2 How to Run a Model with GAMS/MINOS . . . . . . . . . . . . . . . . . . . . . . . . 334 3 Overview of GAMS/MINOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 3.1 Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 3.2 Problems with a Nonlinear Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336 3.3 Problems with Nonlinear Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 4 Modeling Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 4.1 Starting Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338 4.2 Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 4.3 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 4.4 The Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 5 GAMS Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340 5.1 Options Specified through the Option Statement . . . . . . . . . . . . . . . . . . . . . . 340 5.2 Options Specified through Model Suffixes . . . . . . . . . . . . . . . . . . . . . . . . . . 342 6 Summary of MINOS Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 6.1 Output Related Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 6.2 Options Affecting Tolerances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 6.3 Options Affecting Iteration Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 6.4 Other Algorithmic Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 6.5 Examples of GAMS/MINOS Option File . . . . . . . . . . . . . . . . . . . . . . . . . . 344 7 Special Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 7.1 Modeling Hints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 7.2 Storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 8 The GAMS/MINOS Log File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 8.1 Linear Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 8.2 Linearly Constrained NLP’s . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 8.3 NLP’s with Nonlinear Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 9 Detailed Description of MINOS Options . . . . . . . . . . . . . . . . . . . . . . . . . 349 10 Exit Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 360
Page 1:
GAMS — The Solver Manuals c○ 20
Page 4 and 5:
4 CONTENTS
Page 6 and 7:
6 Basic Solver Usage Option iterlim
Page 8 and 9:
8 Basic Solver Usage keyword(s) [mo
Page 10 and 11:
10 AlphaECP 1.2 Running GAMS/AlphaE
Page 12 and 13:
12 AlphaECP A: Infeasible, deleted
Page 14 and 15:
14 AlphaECP 0 Never. 1 Call the NLP
Page 16 and 17:
16 AlphaECP mipoptcr (real) Relativ
Page 18 and 19:
18 AlphaECP Westerlund T. and Pette
Page 20 and 21:
20 BARON 1.1 Licensing and software
Page 22 and 23:
22 BARON on probing : 000:00:00, in
Page 24 and 25:
24 BARON 4.2 Finding the best, seco
Page 26 and 27:
26 BARON in the form of solver boun
Page 28 and 29:
28 BARON 5.4 Range reduction option
Page 30 and 31:
30 BARON 5.6 Heuristic local search
Page 32 and 33:
32 BARON Option Description Default
Page 34 and 35:
34 BDMLP
Page 36 and 37:
36 BENCH option modeltype=bench; Th
Page 38 and 39:
38 BENCH Option Description Default
Page 40 and 41:
40 BENCH In the example below, EXAM
Page 42 and 43:
42 BENCH --- Spawning solver : CPLE
Page 44 and 45:
44 BENCH To terminate not only the
Page 46 and 47:
46 BENCH
Page 48 and 49:
48 COIN-OR also found their way int
Page 50 and 51:
50 COIN-OR This sets the algorithm
Page 52 and 53:
52 COIN-OR Option type default B-BB
Page 54 and 55:
54 COIN-OR integer tolerance: Set i
Page 56 and 57:
56 COIN-OR stop diving on cutoff: F
Page 58 and 59:
58 COIN-OR userjobid: Postfixes gdx
Page 60 and 61:
60 COIN-OR nlp solve max depth: Set
Page 62 and 63:
62 COIN-OR maxmin crit no sol: Weig
Page 64 and 65:
64 COIN-OR General Options iterlim
Page 66 and 67:
66 COIN-OR This option causes GAMS
Page 68 and 69:
68 COIN-OR scaling (string) Scaling
Page 70 and 71:
70 COIN-OR sos This option let CBC
Page 72 and 73:
72 COIN-OR knapsackcuts (string) De
Page 74 and 75:
74 COIN-OR 1 Turns the feasibility
Page 76 and 77:
76 COIN-OR userheurmult (integer) D
Page 78 and 79:
78 COIN-OR This option determines t
Page 80 and 81:
80 COIN-OR noiterlim (integer) Allo
Page 82 and 83:
82 COIN-OR linear_solver pardiso pa
Page 84 and 85:
84 COIN-OR # This is a comment # Tu
Page 86 and 87:
86 COIN-OR acceptable tol: ”Accep
Page 88 and 89:
88 COIN-OR slack bound push: Desire
Page 90 and 91:
90 COIN-OR linear system scaling: M
Page 92 and 93:
92 COIN-OR mumps pivtol: Pivot tole
Page 94 and 95:
94 COIN-OR fixed variable treatment
Page 96 and 97:
96 COIN-OR adaptive mu monotone ini
Page 98 and 99:
98 COIN-OR quality function balanci
Page 100 and 101:
100 COIN-OR alpha min frac: Safety
Page 102 and 103:
102 COIN-OR • no: don’t skip
Page 104 and 105:
104 COIN-OR max resto iter: Maximum
Page 106 and 107:
106 COIN-OR • no: perturbation on
Page 108 and 109:
108 COIN-OR 6.2 Usage of CoinScip T
Page 110 and 111:
110 COIN-OR gams/usergdxname (strin
Page 112 and 113:
112 CONOPT 1 Introduction Nonlinear
Page 114 and 115:
114 CONOPT 130 0 103 2.1776589484E+
Page 116 and 117:
116 CONOPT The two messages above t
Page 118 and 119:
118 CONOPT CONOPT time Total 0.109
Page 120 and 121:
120 CONOPT 6 Hints on Good Model Fo
Page 122 and 123:
122 CONOPT by the reduced complexit
Page 124 and 125:
124 CONOPT of 4.1**2, which means t
Page 126 and 127:
126 CONOPT X.SCALE(I,J,K)$IJK(I,J,K
Page 128 and 129:
128 CONOPT delta. The error is redu
Page 130 and 131:
130 CONOPT the NLP solver will comp
Page 132 and 133:
132 CONOPT The relationship between
Page 134 and 135:
134 CONOPT A : ❅ ❅ ❅ ❅ Zero
Page 136 and 137:
136 CONOPT EQUATION E1, E2, E3, E4;
Page 138 and 139:
138 CONOPT unfortunate that a redun
Page 140 and 141:
140 CONOPT The Crash procedure is n
Page 142 and 143:
142 CONOPT infeasible equations (th
Page 144 and 145:
144 CONOPT The steepest edge proced
Page 146 and 147:
146 CONOPT CONOPT will after the pr
Page 148 and 149:
148 CONOPT X2 appearing in E2: Pivo
Page 150 and 151:
150 CONOPT Until a final solution h
Page 152 and 153:
152 CONOPT Option Description Defau
Page 154 and 155:
154 CONOPT Option Description Defau
Page 156 and 157:
156 CONOPT
Page 158 and 159:
158 CONVERT • LindoMPI • LINGO
Page 160 and 161:
160 CONVERT Option Description Defa
Page 162 and 163:
162 CPLEX 11 2 How to Run a Model w
Page 164 and 165:
164 CPLEX 11 • You can collect al
Page 166 and 167:
166 CPLEX 11 individual GDX solutio
Page 168 and 169:
168 CPLEX 11 parallelmode predual p
Page 170 and 171:
170 CPLEX 11 mipordind mipordtype m
Page 172 and 173:
172 CPLEX 11 6 Special Notes 6.1 Ph
Page 174 and 175:
174 CPLEX 11 Start. The number of t
Page 176 and 177:
176 CPLEX 11 Aggregator did 30 subs
Page 178 and 179:
178 CPLEX 11 Nodes Cuts/ Node Left
Page 180 and 181:
180 CPLEX 11 baritlim (integer) Det
Page 182 and 183:
182 CPLEX 11 -1 Do not generate cov
Page 184 and 185:
184 CPLEX 11 .divflt (real) A diver
Page 186 and 187:
186 CPLEX 11 feasoptmode (integer)
Page 188 and 189:
188 CPLEX 11 implbd (integer) Deter
Page 190 and 191:
190 CPLEX 11 wasted computation tim
Page 192 and 193:
192 CPLEX 11 netppriind (integer) N
Page 194 and 195:
194 CPLEX 11 Settings of this paral
Page 196 and 197:
196 CPLEX 11 1 Force node presolve
Page 198 and 199:
198 CPLEX 11 repairtries (integer)
Page 200 and 201:
200 CPLEX 11 simdisplay (integer) T
Page 202 and 203:
202 CPLEX 11 populate procedure aga
Page 204 and 205:
204 CPLEX 11 1 Display standard min
Page 206 and 207:
206 CPLEX 11
Page 208 and 209:
208 DECIS 1 DECIS 1.1 Introduction
Page 210 and 211:
210 DECIS 1.4 Solving the Universe
Page 212 and 213:
212 DECIS SMPS (stochastic mathemat
Page 214 and 215:
214 DECIS statement would work as w
Page 216 and 217:
216 DECIS RHS demand m 1000.00 peri
Page 218 and 219:
218 DECIS omega1 defines all realiz
Page 220 and 221:
220 DECIS nzrows — Number of rows
Page 222 and 223:
222 DECIS Example The following exa
Page 224 and 225:
224 DECIS 20 0.2464E+05 0.2464E+05
Page 226 and 227:
226 DECIS pro 0.1 0.2 0.5 0.1 0.1 ;
Page 228 and 229:
228 DECIS parameter hm2(dl) / h 100
Page 230 and 231:
230 DECIS 15. ERROR: error code: in
Page 232 and 233:
232 DECIS REFERENCES DECIS License
Page 234 and 235:
234 DICOPT Although the algorithm h
Page 236 and 237:
236 DICOPT where λ i is the Lagran
Page 238 and 239:
238 DICOPT option rminlp=minos; sol
Page 240 and 241:
240 DICOPT **** ERRORS(S) IN EQUATI
Page 242 and 243:
242 DICOPT * stop only on infeasibl
Page 244 and 245:
244 DICOPT mipoptfile s 1 s 2 . . .
Page 246 and 247:
246 DICOPT nlptracelevel 3 As nlptr
Page 248 and 249:
248 DICOPT --- DICOPT: Checking con
Page 250 and 251:
250 DICOPT NLP 2 1.72097< 0.00 3 0
Page 252 and 253:
252 DICOPT • The GAMS option stat
Page 254 and 255:
254 DICOPT REFERENCES
Page 256 and 257:
256 EMP
Page 258 and 259:
258 EXAMINER The optimality checks
Page 260 and 261:
260 EXAMINER Option Description Def
Page 262 and 263:
262 EXAMINER
Page 264 and 265:
264 GAMS/AMPL --- No AmplPath optio
Page 266 and 267:
266 GAMS/LINGO --- No LingoPath opt
Page 268 and 269:
268 KNITRO • Derivative-free, 1st
Page 270 and 271:
270 KNITRO Option Description Defau
Page 272 and 273:
272 KNITRO Option Description Defau
Page 274 and 275:
274 KNITRO If outlev=2, information
Page 276 and 277:
276 KNITRO (Dense) Quasi-Newton BFG
Page 278 and 279:
KNITRO References [1] R. H. Byrd, J
Page 280 and 281:
280 LGO GAMS/LGO does not rely on a
Page 282 and 283: 282 LGO 3 The GAMS/LGO Log File The
Page 284 and 285: 284 LGO Illustrative References R.
Page 286 and 287: 286 LINDOGlobal report the best one
Page 288 and 289: 288 LINDOGlobal Infeasibility of be
Page 290 and 291: 290 LINDOGlobal MIP AOPTTIMLIM time
Page 292 and 293: 292 LINDOGlobal 4.5 NLP Options NLP
Page 294 and 295: 294 LINDOGlobal 3 Decomposed model
Page 296 and 297: 296 LINDOGlobal SOLVER USECUTOFFVAL
Page 298 and 299: 298 LINDOGlobal 0 Solver decides 1
Page 300 and 301: 300 LINDOGlobal MIP INTTOL (real) A
Page 302 and 303: 302 LINDOGlobal MIP HEULEVEL (integ
Page 304 and 305: 304 LINDOGlobal MIP BRANCH PRIO (in
Page 306 and 307: 306 LINDOGlobal 1 Base MIP calculat
Page 308 and 309: 308 LINDOGlobal POSTLEVEL (integer)
Page 310 and 311: 310 LINDOGlobal
Page 312 and 313: 312 LogMIP
Page 314 and 315: 314 MILES When l = −∞ and u =
Page 316 and 317: 316 MILES Given z, MILES uses the v
Page 318 and 319: 318 MILES Pivoting Rules When z 0 e
Page 320 and 321: 320 MILES Pivot Selection Option De
Page 322 and 323: 322 MILES Table 2 Sample Iteration
Page 324 and 325: 324 MILES INFEAS. PIVOTS IN/OUT is
Page 326 and 327: 326 MILES Table 4 Status File with
Page 328 and 329: 328 MILES Table 6 Status File with
Page 330 and 331: 330 MILES N.H. Josephy, ”Newton
Page 334 and 335: 334 MINOS 1 Introduction This docum
Page 336 and 337: 336 MINOS their bounds: l j − δ
Page 338 and 339: 338 MINOS 4 Modeling Issues Formula
Page 340 and 341: 340 MINOS obj.. z =e= sum(i, sqr(re
Page 342 and 343: 342 MINOS reform This option will i
Page 344 and 345: 344 MINOS 6.5 Examples of GAMS/MINO
Page 346 and 347: 346 MINOS B. A. Murtagh, University
Page 348 and 349: 348 MINOS MINOS-Link May 25, 2002 W
Page 350 and 351: 350 MINOS crash option 2 Only the c
Page 352 and 353: 352 MINOS The storage required if o
Page 354 and 355: 354 MINOS linear variables y or the
Page 356 and 357: 356 MINOS Pivot Tolerance r Broadly
Page 358 and 359: 358 MINOS Solution yes Solution no
Page 360 and 361: 360 MINOS Verify option 3 A detaile
Page 362 and 363: 362 MINOS The simple way to solve t
Page 364 and 365: 364 MINOS REFERENCES
Page 366 and 367: 366 MOSEK Furthermore, MOSEK can so
Page 368 and 369: 368 MOSEK 1.5 Nonlinear Programs MO
Page 370 and 371: 370 MOSEK The original problem is:
Page 372 and 373: 372 MOSEK The first option specifie
Page 374 and 375: 374 MOSEK MSK IPAR LOG BI output co
Page 376 and 377: 376 MOSEK MSK IPAR SIM PRIMAL CRASH
Page 378 and 379: 378 MOSEK MSK IPAR PRESOLVE LINDEP
Page 380 and 381: 380 MOSEK MSK DPAR DATA TOL QIJ (re
Page 382 and 383:
382 MOSEK MSK DPAR INTPNT CO TOL NE
Page 384 and 385:
384 MOSEK MSK IPAR INTPNT STARTING
Page 386 and 387:
386 MOSEK MSK IPAR SIM PRIMAL CRASH
Page 388 and 389:
388 MOSEK Coefficient reduction +51
Page 390 and 391:
390 MOSEK 8 The optimizer for nonco
Page 392 and 393:
392 MOSEK Simplex - cpu time: 0.00
Page 394 and 395:
394 MOSEK Presolve - time : 0.00 Pr
Page 396 and 397:
396 MPSWRITE need to use applicatio
Page 398 and 399:
398 MPSWRITE C0000004 X SAN-DIEGO N
Page 400 and 401:
400 MPSWRITE 6 Using Analyze with M
Page 402 and 403:
402 MPSWRITE 7 The GAMS/MPSWRITE Op
Page 404 and 405:
404 MPSWRITE 8 Name Generation Exam
Page 406 and 407:
406 MPSWRITE C CHICAGO T TOPEKA I c
Page 408 and 409:
408 MPSWRITE the macros @i and @j.
Page 410 and 411:
410 MPSWRITE SSA 3 -0.10000000E+21
Page 412 and 413:
412 MPSWRITE < translate .lp file i
Page 414 and 415:
414 MPSWRITE ANALYZE Version 10.0 b
Page 416 and 417:
416 NLPEC gams nash MPEC=nlpec MCP=
Page 418 and 419:
418 NLPEC Note that each inner prod
Page 420 and 421:
420 NLPEC Note that the slack varia
Page 422 and 423:
422 NLPEC 3.2.1 Doubly bounded vari
Page 424 and 425:
424 NLPEC Option Description Defaul
Page 426 and 427:
426 NLPEC L/U B equref reftype sign
Page 428 and 429:
428 NLPEC
Page 430 and 431:
430 OQNLP and MSNLP the set gathere
Page 432 and 433:
432 OQNLP and MSNLP Itn Penval Meri
Page 434 and 435:
434 OQNLP and MSNLP this guide. You
Page 436 and 437:
436 OQNLP and MSNLP Option Descript
Page 438 and 439:
438 OQNLP and MSNLP ENDDO xt ∗ =
Page 440 and 441:
440 OQNLP and MSNLP Return xt This
Page 442 and 443:
442 OSL 3 Overview of OSL OSL offer
Page 444 and 445:
444 OSL sets the amount of memory u
Page 446 and 447:
446 OSL 5.5 Examples of GAMS/OSL Op
Page 448 and 449:
448 OSL Option Description Default
Page 450 and 451:
450 OSL Option Description Default
Page 452 and 453:
452 OSL 7 Special Notes This sectio
Page 454 and 455:
454 OSL 9 Examples of MPS Files Wri
Page 456 and 457:
456 OSL XU C0000004 R0000004 0.0000
Page 458 and 459:
458 OSL Stochastic Extensions The s
Page 460 and 461:
460 OSL Stochastic Extensions 4.2 M
Page 462 and 463:
462 OSL Stochastic Extensions Optio
Page 464 and 465:
464 OSL Stochastic Extensions
Page 466 and 467:
466 PATH 4.6 3.3 Difficult Models .
Page 468 and 469:
468 PATH 4.6 sets i canning plants,
Page 470 and 471:
470 PATH 4.6 $include walras.dat po
Page 472 and 473:
472 PATH 4.6 An advantage of the ex
Page 474 and 475:
474 PATH 4.6 S O L V E S U M M A R
Page 476 and 477:
476 PATH 4.6 which has a unique sol
Page 478 and 479:
478 PATH 4.6 --- Starting compilati
Page 480 and 481:
480 PATH 4.6 Code Meaning C A cycle
Page 482 and 483:
482 PATH 4.6 At the end of the log
Page 484 and 485:
484 PATH 4.6 Option Default Explana
Page 486 and 487:
486 PATH 4.6 2.6 Preprocessing The
Page 488 and 489:
488 PATH 4.6 In the context of nonl
Page 490 and 491:
490 PATH 4.6 the generated path ema
Page 492 and 493:
492 PATH 4.6 Figure 28.10: Merit Fu
Page 494 and 495:
494 PATH 4.6 INITIAL POINT STATISTI
Page 496 and 497:
496 PATH 4.6 A.1 Classical Model Th
Page 498 and 499:
498 PATH 4.6 7 orders of magnitude
Page 500 and 501:
PATH References [1] S. C. Billups.
Page 502 and 503:
502 PATH REFERENCES
Page 504 and 505:
504 PATHNLP The standard GAMS model
Page 506 and 507:
506 SBB • March 21, 2001: Level 0
Page 508 and 509:
508 SBB Option Description Default
Page 510 and 511:
510 SBB Solution satisfies optcr St
Page 512 and 513:
512 SBB Non convex model! # jumps i
Page 514 and 515:
514 SCENRED running time) of the me
Page 516 and 517:
516 SCENRED eter stored in the SCEN
Page 518 and 519:
518 SCENRED 6 The SCENRED Output Fi
Page 520 and 521:
520 SCENRED 9 SCENRED Warnings SCEN
Page 522 and 523:
522 SNOPT to be stated in the form
Page 524 and 525:
524 SNOPT 2.2 Constraints and slack
Page 526 and 527:
526 SNOPT By analogy with the eleme
Page 528 and 529:
528 SNOPT * fill with random data x
Page 530 and 531:
530 SNOPT 4 Options In many cases N
Page 532 and 533:
532 SNOPT of (nonlinear) nonzeroes,
Page 534 and 535:
534 SNOPT In all cases, a derivativ
Page 536 and 537:
536 SNOPT • t must be a real valu
Page 538 and 539:
538 SNOPT s a single line that give
Page 540 and 541:
540 SNOPT • It is common for two
Page 542 and 543:
542 SNOPT Unbounded objective value
Page 544 and 545:
544 SNOPT --- Starting execution --
Page 546 and 547:
546 SNOPT Merit is the value of the
Page 548 and 549:
548 SNOPT EXIT -- Requested accurac
Page 550 and 551:
550 SNOPT EXIT -- Function evaluati
Page 552 and 553:
552 SNOPT The possible EXIT message
Page 554 and 555:
554 PATH REFERENCES
Page 556 and 557:
556 XA during the course of program
Page 558 and 559:
558 XA Each XA B&B strategy has man
Page 560 and 561:
560 XA Option Description Default D
Page 562 and 563:
562 XA Option Description Default S
Page 564 and 565:
564 XA
Page 566 and 567:
566 XPRESS • option iterlim=n; or
Page 568 and 569:
568 XPRESS Option Description Defau
Page 570 and 571:
Page 572 and 573:
Page 574:
574 XPRESS
show all

GAMS â The Solver Manuals - Available Software

Create successful ePaper yourself

Delete template?

Save as template?

GAMS â The Solver Manuals - Available Software