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254 DICOPT REFERENCES
EMP Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 1 Introduction EMP (Extended Mathematical Programming) is an experimental framework for automated mathematical programming reformulations as • Bilevel Programs • Disjunctive Programs • Extended Nonlinear Programs • Programs with side constraints/variables • ... The idea behind EMP is that new upcoming model types which cannot be solved reliably yet are reformulated into model of established math programming classes in order to use mature solver technology. EMP has been developed jointly by Michael Ferris of UW-Madison, Ignacio Grossmann of Carnegie Mellon University and GAMS Development Corporation. EMP comes free of charge with any licensed GAMS system but needs a subsolver to solve the generated models.
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GAMS — The Solver Manuals c○ 20
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4 CONTENTS
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6 Basic Solver Usage Option iterlim
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8 Basic Solver Usage keyword(s) [mo
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10 AlphaECP 1.2 Running GAMS/AlphaE
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12 AlphaECP A: Infeasible, deleted
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14 AlphaECP 0 Never. 1 Call the NLP
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16 AlphaECP mipoptcr (real) Relativ
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18 AlphaECP Westerlund T. and Pette
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20 BARON 1.1 Licensing and software
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22 BARON on probing : 000:00:00, in
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24 BARON 4.2 Finding the best, seco
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26 BARON in the form of solver boun
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28 BARON 5.4 Range reduction option
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30 BARON 5.6 Heuristic local search
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32 BARON Option Description Default
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34 BDMLP
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36 BENCH option modeltype=bench; Th
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38 BENCH Option Description Default
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40 BENCH In the example below, EXAM
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42 BENCH --- Spawning solver : CPLE
Page 44 and 45:
44 BENCH To terminate not only the
Page 46 and 47:
46 BENCH
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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
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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
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66 COIN-OR This option causes GAMS
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68 COIN-OR scaling (string) Scaling
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70 COIN-OR sos This option let CBC
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72 COIN-OR knapsackcuts (string) De
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74 COIN-OR 1 Turns the feasibility
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76 COIN-OR userheurmult (integer) D
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78 COIN-OR This option determines t
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80 COIN-OR noiterlim (integer) Allo
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82 COIN-OR linear_solver pardiso pa
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84 COIN-OR # This is a comment # Tu
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86 COIN-OR acceptable tol: ”Accep
Page 88 and 89:
88 COIN-OR slack bound push: Desire
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90 COIN-OR linear system scaling: M
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92 COIN-OR mumps pivtol: Pivot tole
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94 COIN-OR fixed variable treatment
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96 COIN-OR adaptive mu monotone ini
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98 COIN-OR quality function balanci
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100 COIN-OR alpha min frac: Safety
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102 COIN-OR • no: don’t skip
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104 COIN-OR max resto iter: Maximum
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106 COIN-OR • no: perturbation on
Page 108 and 109:
108 COIN-OR 6.2 Usage of CoinScip T
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110 COIN-OR gams/usergdxname (strin
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112 CONOPT 1 Introduction Nonlinear
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114 CONOPT 130 0 103 2.1776589484E+
Page 116 and 117:
116 CONOPT The two messages above t
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118 CONOPT CONOPT time Total 0.109
Page 120 and 121:
120 CONOPT 6 Hints on Good Model Fo
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122 CONOPT by the reduced complexit
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124 CONOPT of 4.1**2, which means t
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126 CONOPT X.SCALE(I,J,K)$IJK(I,J,K
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128 CONOPT delta. The error is redu
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130 CONOPT the NLP solver will comp
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132 CONOPT The relationship between
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134 CONOPT A : ❅ ❅ ❅ ❅ Zero
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136 CONOPT EQUATION E1, E2, E3, E4;
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138 CONOPT unfortunate that a redun
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140 CONOPT The Crash procedure is n
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142 CONOPT infeasible equations (th
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144 CONOPT The steepest edge proced
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146 CONOPT CONOPT will after the pr
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148 CONOPT X2 appearing in E2: Pivo
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150 CONOPT Until a final solution h
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152 CONOPT Option Description Defau
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154 CONOPT Option Description Defau
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156 CONOPT
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158 CONVERT • LindoMPI • LINGO
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160 CONVERT Option Description Defa
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162 CPLEX 11 2 How to Run a Model w
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164 CPLEX 11 • You can collect al
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166 CPLEX 11 individual GDX solutio
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168 CPLEX 11 parallelmode predual p
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170 CPLEX 11 mipordind mipordtype m
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172 CPLEX 11 6 Special Notes 6.1 Ph
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174 CPLEX 11 Start. The number of t
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176 CPLEX 11 Aggregator did 30 subs
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178 CPLEX 11 Nodes Cuts/ Node Left
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180 CPLEX 11 baritlim (integer) Det
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182 CPLEX 11 -1 Do not generate cov
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184 CPLEX 11 .divflt (real) A diver
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186 CPLEX 11 feasoptmode (integer)
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188 CPLEX 11 implbd (integer) Deter
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190 CPLEX 11 wasted computation tim
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192 CPLEX 11 netppriind (integer) N
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194 CPLEX 11 Settings of this paral
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196 CPLEX 11 1 Force node presolve
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198 CPLEX 11 repairtries (integer)
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200 CPLEX 11 simdisplay (integer) T
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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 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
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304 LINDOGlobal MIP BRANCH PRIO (in
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306 LINDOGlobal 1 Base MIP calculat
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308 LINDOGlobal POSTLEVEL (integer)
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310 LINDOGlobal
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312 LogMIP
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314 MILES When l = −∞ and u =
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316 MILES Given z, MILES uses the v
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318 MILES Pivoting Rules When z 0 e
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320 MILES Pivot Selection Option De
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322 MILES Table 2 Sample Iteration
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324 MILES INFEAS. PIVOTS IN/OUT is
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326 MILES Table 4 Status File with
Page 328 and 329:
328 MILES Table 6 Status File with
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330 MILES N.H. Josephy, ”Newton
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332 MILES Table 8 Transport Model i
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334 MINOS 1 Introduction This docum
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336 MINOS their bounds: l j − δ
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338 MINOS 4 Modeling Issues Formula
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340 MINOS obj.. z =e= sum(i, sqr(re
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342 MINOS reform This option will i
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344 MINOS 6.5 Examples of GAMS/MINO
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346 MINOS B. A. Murtagh, University
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348 MINOS MINOS-Link May 25, 2002 W
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350 MINOS crash option 2 Only the c
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352 MINOS The storage required if o
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354 MINOS linear variables y or the
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356 MINOS Pivot Tolerance r Broadly
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358 MINOS Solution yes Solution no
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360 MINOS Verify option 3 A detaile
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362 MINOS The simple way to solve t
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364 MINOS REFERENCES
Page 366 and 367:
366 MOSEK Furthermore, MOSEK can so
Page 368 and 369:
368 MOSEK 1.5 Nonlinear Programs MO
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370 MOSEK The original problem is:
Page 372 and 373:
372 MOSEK The first option specifie
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374 MOSEK MSK IPAR LOG BI output co
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376 MOSEK MSK IPAR SIM PRIMAL CRASH
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378 MOSEK MSK IPAR PRESOLVE LINDEP
Page 380 and 381:
380 MOSEK MSK DPAR DATA TOL QIJ (re
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382 MOSEK MSK DPAR INTPNT CO TOL NE
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384 MOSEK MSK IPAR INTPNT STARTING
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386 MOSEK MSK IPAR SIM PRIMAL CRASH
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388 MOSEK Coefficient reduction +51
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390 MOSEK 8 The optimizer for nonco
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392 MOSEK Simplex - cpu time: 0.00
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394 MOSEK Presolve - time : 0.00 Pr
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396 MPSWRITE need to use applicatio
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398 MPSWRITE C0000004 X SAN-DIEGO N
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400 MPSWRITE 6 Using Analyze with M
Page 402 and 403:
402 MPSWRITE 7 The GAMS/MPSWRITE Op
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404 MPSWRITE 8 Name Generation Exam
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406 MPSWRITE C CHICAGO T TOPEKA I c
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408 MPSWRITE the macros @i and @j.
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410 MPSWRITE SSA 3 -0.10000000E+21
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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=
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418 NLPEC Note that each inner prod
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420 NLPEC Note that the slack varia
Page 422 and 423:
422 NLPEC 3.2.1 Doubly bounded vari
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424 NLPEC Option Description Defaul
Page 426 and 427:
426 NLPEC L/U B equref reftype sign
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428 NLPEC
Page 430 and 431:
430 OQNLP and MSNLP the set gathere
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432 OQNLP and MSNLP Itn Penval Meri
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434 OQNLP and MSNLP this guide. You
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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
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442 OSL 3 Overview of OSL OSL offer
Page 444 and 445:
444 OSL sets the amount of memory u
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446 OSL 5.5 Examples of GAMS/OSL Op
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448 OSL Option Description Default
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450 OSL Option Description Default
Page 452 and 453:
452 OSL 7 Special Notes This sectio
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454 OSL 9 Examples of MPS Files Wri
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456 OSL XU C0000004 R0000004 0.0000
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458 OSL Stochastic Extensions The s
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460 OSL Stochastic Extensions 4.2 M
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462 OSL Stochastic Extensions Optio
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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,
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470 PATH 4.6 $include walras.dat po
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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
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480 PATH 4.6 Code Meaning C A cycle
Page 482 and 483:
482 PATH 4.6 At the end of the log
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484 PATH 4.6 Option Default Explana
Page 486 and 487:
486 PATH 4.6 2.6 Preprocessing The
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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
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496 PATH 4.6 A.1 Classical Model Th
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498 PATH 4.6 7 orders of magnitude
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PATH References [1] S. C. Billups.
Page 502 and 503:
502 PATH REFERENCES
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504 PATHNLP The standard GAMS model
Page 506 and 507:
506 SBB • March 21, 2001: Level 0
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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
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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,
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534 SNOPT In all cases, a derivativ
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536 SNOPT • t must be a real valu
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538 SNOPT s a single line that give
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540 SNOPT • It is common for two
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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
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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
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560 XA Option Description Default D
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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:
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574 XPRESS
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