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64 COIN-OR General Options iterlim names reslim special writemps iteration limit specifies whether variable and equation names should be given to CBC resource limit options passed unseen to CBC create MPS file for problem LP Options idiotcrash sprintcrash sifting crash maxfactor crossover dualpivot primalpivot perturbation scaling presolve tol dual tol primal tol presolve startalg idiot crash sprint crash synonym for sprint crash use crash method to get dual feasible maximum number of iterations between refactorizations crossover to simplex algorithm after barrier dual pivot choice algorithm primal pivot choice algorithm perturbation of problem scaling method switch for initial presolve of LP dual feasibility tolerance primal feasibility tolerance tolerance used in presolve LP solver for root node MIP Options tol integer sollim strongbranching trustpseudocosts coststrategy nodestrategy preprocess printfrequency increment nodelim nodlim optca optcr cutoff tolerance for integrality limit on number of solutions strong branching after howmany nodes we trust the pseudo costs how to use costs as priorities how to select nodes integer presolve frequency of status prints increment of cutoff when new incumbent node limit node limit absolute stopping tolerance relative stopping tolerance cutoff for objective function value MIP Options for Cutting Plane Generators cutdepth cut passes root cut passes tree cuts cliquecuts flowcovercuts depth in tree at which cuts are applied number of cut passes at root node number of cut passes at nodes in the tree global switch for cutgenerators Clique Cuts Flow Cover Cuts
COIN-OR 65 gomorycuts knapsackcuts liftandprojectcuts mircuts twomircuts probingcuts reduceandsplitcuts residualcapacitycuts Gomory Cuts Knapsack Cover Cuts Lift and Project Cuts Mixed Integer Rounding Cuts Two Phase Mixed Integer Rounding Cuts Probing Cuts Reduce and Split Cuts Residual Capacity Cuts MIP Options for Heuristics heuristics combinesolutions feaspump feaspump passes greedyheuristic localtreesearch rins roundingheuristic global switch for heuristics combine solutions heuristic feasibility pump number of feasibility passes greedy heuristic local tree search heuristic relaxed induced neighborhood search rounding heuristic MIP Options for the GAMS Branch Cut and Heuristic Facility usercutcall usercutfirst usercutfreq usercutinterval usercutmult usercutnewint usergdxin usergdxname usergdxnameinc usergdxprefix userheurcall userheurfirst userheurfreq userheurinterval userheurmult userheurnewint userheurobjfirst userjobid userkeep The GAMS command line to call the cut generator Calls the cut generator for the first n nodes Determines the frequency of the cut generator model calls Determines interval when to apply multiplier for frequency of cut generator model calls Determines the multiplier for the frequency of the cut generator model calls Calls the cut generator if the solver found a new integer feasible solution The name of the GDX file read back into CBC The name of the GDX file exported from the solver with the solution at the node The name of the GDX file exported from the solver with the incumbent solution Prefixes usergdxin, usergdxname, and usergdxnameinc The GAMS command line to call the heuristic Calls the heuristic for the first n nodes Determines the frequency of the heuristic model calls Determines interval when to apply multiplier for frequency of heuristic model calls Determines the multiplier for the frequency of the heuristic model calls Calls the heuristic if the solver found a new integer feasible solution Calls heuristic if LP value of node is closer to best bound than current incumbent Postfixes gdxname, gdxnameinc and gdxin Calls gamskeep instead of gams 3.4 Detailed Descriptions of CoinCBC Options General Options iterlim (integer) For an LP, this is the maximum number of iterations to solve the LP. For a MIP, this option is ignored. (default = GAMS iterlim) names (integer)
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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
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 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
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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
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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
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204 CPLEX 11 1 Display standard min
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206 CPLEX 11
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208 DECIS 1 DECIS 1.1 Introduction
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210 DECIS 1.4 Solving the Universe
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212 DECIS SMPS (stochastic mathemat
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214 DECIS statement would work as w
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216 DECIS RHS demand m 1000.00 peri
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218 DECIS omega1 defines all realiz
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220 DECIS nzrows — Number of rows
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222 DECIS Example The following exa
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224 DECIS 20 0.2464E+05 0.2464E+05
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226 DECIS pro 0.1 0.2 0.5 0.1 0.1 ;
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228 DECIS parameter hm2(dl) / h 100
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230 DECIS 15. ERROR: error code: in
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232 DECIS REFERENCES DECIS License
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234 DICOPT Although the algorithm h
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236 DICOPT where λ i is the Lagran
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238 DICOPT option rminlp=minos; sol
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240 DICOPT **** ERRORS(S) IN EQUATI
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242 DICOPT * stop only on infeasibl
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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
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250 DICOPT NLP 2 1.72097< 0.00 3 0
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252 DICOPT • The GAMS option stat
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254 DICOPT REFERENCES
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256 EMP
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258 EXAMINER The optimality checks
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260 EXAMINER Option Description Def
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262 EXAMINER
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264 GAMS/AMPL --- No AmplPath optio
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266 GAMS/LINGO --- No LingoPath opt
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268 KNITRO • Derivative-free, 1st
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270 KNITRO Option Description Defau
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272 KNITRO Option Description Defau
Page 274 and 275:
274 KNITRO If outlev=2, information
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276 KNITRO (Dense) Quasi-Newton BFG
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KNITRO References [1] R. H. Byrd, J
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280 LGO GAMS/LGO does not rely on a
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282 LGO 3 The GAMS/LGO Log File The
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284 LGO Illustrative References R.
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286 LINDOGlobal report the best one
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288 LINDOGlobal Infeasibility of be
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290 LINDOGlobal MIP AOPTTIMLIM time
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292 LINDOGlobal 4.5 NLP Options NLP
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294 LINDOGlobal 3 Decomposed model
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296 LINDOGlobal SOLVER USECUTOFFVAL
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298 LINDOGlobal 0 Solver decides 1
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300 LINDOGlobal MIP INTTOL (real) A
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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
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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
Page 354 and 355:
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
Page 370 and 371:
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
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414 MPSWRITE ANALYZE Version 10.0 b
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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
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422 NLPEC 3.2.1 Doubly bounded vari
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424 NLPEC Option Description Defaul
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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 ∗ =
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440 OQNLP and MSNLP Return xt This
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442 OSL 3 Overview of OSL OSL offer
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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
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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
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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
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474 PATH 4.6 S O L V E S U M M A R
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476 PATH 4.6 which has a unique sol
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478 PATH 4.6 --- Starting compilati
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480 PATH 4.6 Code Meaning C A cycle
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482 PATH 4.6 At the end of the log
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484 PATH 4.6 Option Default Explana
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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
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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.
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502 PATH REFERENCES
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504 PATHNLP The standard GAMS model
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506 SBB • March 21, 2001: Level 0
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508 SBB Option Description Default
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510 SBB Solution satisfies optcr St
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512 SBB Non convex model! # jumps i
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514 SCENRED running time) of the me
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516 SCENRED eter stored in the SCEN
Page 518 and 519:
518 SCENRED 6 The SCENRED Output Fi
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520 SCENRED 9 SCENRED Warnings SCEN
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522 SNOPT to be stated in the form
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524 SNOPT 2.2 Constraints and slack
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526 SNOPT By analogy with the eleme
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528 SNOPT * fill with random data x
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530 SNOPT 4 Options In many cases N
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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
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544 SNOPT --- Starting execution --
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546 SNOPT Merit is the value of the
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548 SNOPT EXIT -- Requested accurac
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550 SNOPT EXIT -- Function evaluati
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552 SNOPT The possible EXIT message
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554 PATH REFERENCES
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556 XA during the course of program
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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
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564 XA
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566 XPRESS • option iterlim=n; or
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568 XPRESS Option Description Defau
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574 XPRESS
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