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444 OSL sets the amount of memory used to 10 MB. Mymodel is the name of the model as specified by the model statement. In order to be effective, the assignment of the model suffix should be made between the model and solve statements. Option workspace optfile cheat cutoff prioropt Description Gives OSL x MB of workspace. Overrides the memory estimation. Instructs OSL to read the option file osl.opt. Cheat value: each new integer solution must be at least x better than the previous one. Can speed up the search, but you may miss the optimal solution. The cheat parameter is specified in absolute terms (like the OPTCA option). The OSL option improve overrides the GAMS cheat parameter. Cutoff value. When the Branch and Bound search starts, the parts of the tree with an objective worse than x are deleted. Can speed up the initial phase in the Branch and Bound. Instructs OSL to use priority branching information passed by GAMS through the variable.prior parameters. 5 Summary of OSL Options All these options should be entered in the option file osl.opt after setting the mymodel.optfile parameter to 1. The option file is not case sensitive and the keywords must be given in full. Examples for using the option file can be found at the end of this section. 5.1 LP Algorithmic Options Option crash iterlim method pdgaptol presolve reslim simopt scale toldinf tolpinf workspace Description crash an initial basis iteration limit solution method barrier method primal-dual gap tolerance perform presolve. Reduce size of model. resource limit simplex option perform scaling dual feasibility tolerance primal feasibility tolerance memory allocation 5.2 MIP Algorithmic Options Option bbpreproc cutoff cuts degscale improve incore iweight maxnodes maxsols optca optcr Description branch and bound preprocessing cutoff or incumbent value allocate space for extra cuts scale factor for degradation required level of improvement over current best integer solution keep tree in core weight for each integer infeasibility maximum number of nodes allowed maximum number of integer solutions allowed absolute optimality criterion relative optimality criterion
OSL 445 Option strategy target threshold tolint Description MIP strategy target value for objective supernode processing threshold integer tolerance 5.3 Screen and Output File Options Option bastype creatbas creatmps iterlog mpstype networklog nodelog Description format of basis file create basis file create MPS file iteration log type of MPS file network log MIP log 5.4 Advanced LP Options Option Description adjac save storage on AA T chabstol absolute pivot tolerance for Cholesky factorization chweight rate of change of multiplier in composite objective function chtinytol cut-off tolerance in Cholesky factorization crossover when to switch to simplex densecol dense column threshold densethr density threshold in Cholesky devex devex pricing method droprowct constraint dropping threshold dweight proportion of feasible objective used in dual infeasible solution factor refactorization frequency fastits switching frequency to devex fixvar1 tolerance for fixing variables in barrier method when infeasible fixvar2 tolerance for fixing variables in barrier method when feasible formntype formation of normal matrix maxprojns maximum number of null space projections muinit initial value for µ mulimit lower limit for µ mulinfac multiple of µ to add to the linear objective mufactor reduction factor for µ in primal barrier method nullcheck null space checking switch objweight weight given to true objective function in phase I of primal barrier pdstepmult step-length multiplier for primal-dual barrier pertdiag diagonal perturbation in Cholesky factorization possbasis potential basis flag postsolve basis solution required projtol projection error tolerance starting value for multiplier in composite objective function pweight rgfactor reduced gradient target reduction rglimit reduced gradient limit simopt simplex option stepmult step-length multiplier for primal barrier algorithm
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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
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242 DICOPT * stop only on infeasibl
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244 DICOPT mipoptfile s 1 s 2 . . .
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246 DICOPT nlptracelevel 3 As nlptr
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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
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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
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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
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
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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 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
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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
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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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GAMS â The Solver Manuals - Available Software