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292 LINDOGlobal 4.5 NLP Options NLP SOLVE AS LP flag indicating if the nonlinear model will be solved as an LP NLP SOLVER type of nonlinear solver NLP SUBSOLVER type of nonlinear subsolver NLP PSTEP FINITEDIFF value of the step length in computing the derivatives using finite differences NLP DERIV DIFFTYPE flag indicating the technique used in computing derivatives with finite differences NLP FEASTOL feasibility tolerance for nonlinear constraints NLP REDGTOL tolerance for the gradients of nonlinear functions NLP USE CRASH flag for using simple crash routines for initial solution NLP USE STEEPEDGE flag for using steepest edge directions for updating solution NLP USE SLP flag for using sequential linear programming step directions for updating solution NLP USE SELCONEVAL flag for using selective constraint evaluations for solving NLP NLP PRELEVEL controls the amount and type of NLP pre-solving NLP ITRLMT nonlinear iteration limit NLP LINEARZ extent to which the solver will attempt to linearize nonlinear models NLP STARTPOINT flag for using initial starting solution for NLP NLP QUADCHK flag for checking if NLP is quadratic NLP AUTODERIV defining type of computing derivatives NLP MAXLOCALSEARCH maximum number of local searches NLP USE LINDO CRASH flag for using advanced crash routines for initial solution NLP STALL ITRLMT iteration limit before a sequence of non-improving NLP iterations is declared as stalling NLP AUTOHESS flag for using Second Order Automatic Differentiation for solving NLP 4.6 Global Options OPTTOL FLTTOL BOXTOL optimality tolerance floating-point tolerance
LINDOGlobal 293 minimal width of variable intervals WIDTOL maximal width of variable intervals DELTATOL delta tolerance in GOP convexification BNDLIM max magnitude of variable bounds used in GOP convexification TIMLIM time limit in seconds for GOP branch-and-bound OPTCHKMD criterion used to certify the global optimality BRANCHMD direction to branch first when branching on a variable MAXWIDMD maximum width flag for the global solution PRELEVEL amount and type of GOP presolving POSTLEVEL amount and type of GOP postsolving BBSRCHMD node selection rule in GOP branch-and-bound DECOMPPTMD decomposition point selection rule in GOP branch-and-bound ALGREFORMMD algebraic reformulation rule for a GOP RELBRNDMD reliable rounding in the GOP branch-and-bound USEBNDLIM max magnitude of variable bounds flag for GOP convexification BRANCH LIMIT limit on the total number of branches to be created in GOP tree CORELEVEL strategy of GOP branch-and-bound OPT MODE mode for GOP optimization HEU MODE heuristic used in global solver SUBOUT MODE substituting out fixed variables LSOLBRANLIM branch limit until finding a new nonlinear solution USEGOP use global optimization 5 Detailed Descriptions of LINDOGLOBAL Options DECOMPOSITION TYPE (integer) This refers to the type of decomposition to be performed on a linear or mixed integer model. (default = 1) 0 <strong>Solver</strong> decides which type of decomposition to use 1 <strong>Solver</strong> does not perform any decompositions and uses the original model 2 Attempt total decomposition
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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
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44 BENCH To terminate not only the
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46 BENCH
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48 COIN-OR also found their way int
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50 COIN-OR This sets the algorithm
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52 COIN-OR Option type default B-BB
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54 COIN-OR integer tolerance: Set i
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56 COIN-OR stop diving on cutoff: F
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58 COIN-OR userjobid: Postfixes gdx
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60 COIN-OR nlp solve max depth: Set
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62 COIN-OR maxmin crit no sol: Weig
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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
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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
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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+
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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
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 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 332 and 333: 332 MILES Table 8 Transport Model i
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
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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
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366 MOSEK Furthermore, MOSEK can so
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368 MOSEK 1.5 Nonlinear Programs MO
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370 MOSEK The original problem is:
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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
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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
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.
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502 PATH REFERENCES
Page 504 and 505:
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
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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
Page 556 and 557:
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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