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372 MOSEK The first option specifies the maximum number of interior-point iterations, in this case 20. The seond option indicates a scaling option of 1, which is no scaling. We remark that users can also use symbolic constants in place of numerical values. For example, for the scaling option users could use MSK SCALING NONE in place of the value 1. For a complete list of applicable symbolic constants, consult the MOSEK parameter list available online at www.mosek.com. 3.1 Memory Considerations for Nonlinear Problems The GAMS workfactor option can be used to increase the amount of memory available to MOSEK. The general syntax is (modelname).workfactor = (value) with a default value of 1. See the section on “Using Solver Specific Options” for details. If GAMS/MOSEK runs out of memory, an error message is printed out: *** GAMS/MOSEK interface error. The size estimate for Hessian of Lagrangian is too small. Try to increase workfactor option from 1 to a larger value. GAMS/MOSEK estimates the size of the Hessian as 5∗(number of nonlinear variables)∗(workfactor). Because of symmetry, the size of the Hessian is bounded by H d ∗ (number of nonlinear variables) 2 /2 where H d detotes the density of the Hessian and H d ∈ [0, 1]. Therefore, one can choose the workfactor as: workfactor = H d ∗ (number of nonlinear variables) ∗ /(5 ∗ 2) Note that for a seperable model (see “Modeling Issues Involving Convex Programs”), the workfactor can in fact be reduced to 1/5. 4 Summary of MOSEK Options 4.1 General and Preprocessing Options MSK IPAR OPTIMIZER optimizer selection MSK DPAR OPTIMIZER MAX TIME time limit MSK IPAR PRESOLVE ELIMINATOR USE switch for free variable elimination MSK IPAR PRESOLVE ELIM FILL fill-in control during presolve MSK IPAR PRESOLVE LINDEP USE linear dependency check MSK IPAR PRESOLVE LINDEP WORK LIM maximum work for finding linear dependencies MSK IPAR PRESOLVE USE switch for presolve MSK IPAR CACHE SIZE L1 L1 cache size used MSK IPAR CACHE SIZE L2
MOSEK 373 L2 cache size used MSK IPAR CPU TYPE specifies the CPU type MSK SPAR PARAM READ FILE NAME name of a secondary MOSEK option file MSK IPAR CONCURRENT NUM OPTIMIZERS maximum number of optimizers during concurrent run MSK IPAR CONCURRENT PRIORITY DUAL SIMPLEX priority of dual simplex algorithm in concurrent run MSK IPAR CONCURRENT PRIORITY FREE SIMPLEX priority of free simplex algorithm in concurrent run MSK IPAR CONCURRENT PRIORITY INTPNT priority of interior point algorithm in concurrent run MSK IPAR CONCURRENT PRIORITY PRIMAL SIMPLEX priority of primal simplex algorithm in concurrent run MSK IPAR INFEAS REPORT AUTO switch for infeasibility report MSK IPAR INFEAS REPORT LEVEL output level for infeasibility report 4.2 Problem Data Options MSK DPAR DATA TOL AIJ zero tolerance for matrix coefficients MSK DPAR DATA TOL AIJ LARGE warning for large coefficients in matrix MSK DPAR DATA TOL BOUND INF bound value for infinity MSK DPAR DATA TOL BOUND WRN warning for large bounds MSK DPAR DATA TOL CJ LARGE warning for large coefficients in objective MSK DPAR DATA TOL C HUGE error for huge coefficients in objective MSK DPAR DATA TOL QIJ zero tolerance for Q matrix coefficients MSK DPAR DATA TOL X tolerance for fixed variables MSK IPAR CHECK CONVEXITY level of convexity check for quadratic problems MSK DPAR LOWER OBJ CUT lower objective limit MSK DPAR LOWER OBJ CUT FINITE TRH upper objective limit threashold MSK DPAR UPPER OBJ CUT upper objective limit MSK DPAR UPPER OBJ CUT FINITE TRH lower objective limit threashold 4.3 Output Options
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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
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
Page 342 and 343: 342 MINOS reform This option will i
Page 344 and 345: 344 MINOS 6.5 Examples of GAMS/MINO
Page 346 and 347: 346 MINOS B. A. Murtagh, University
Page 348 and 349: 348 MINOS MINOS-Link May 25, 2002 W
Page 350 and 351: 350 MINOS crash option 2 Only the c
Page 352 and 353: 352 MINOS The storage required if o
Page 354 and 355: 354 MINOS linear variables y or the
Page 356 and 357: 356 MINOS Pivot Tolerance r Broadly
Page 358 and 359: 358 MINOS Solution yes Solution no
Page 360 and 361: 360 MINOS Verify option 3 A detaile
Page 362 and 363: 362 MINOS The simple way to solve t
Page 364 and 365: 364 MINOS REFERENCES
Page 366 and 367: 366 MOSEK Furthermore, MOSEK can so
Page 368 and 369: 368 MOSEK 1.5 Nonlinear Programs MO
Page 370 and 371: 370 MOSEK The original problem is:
Page 374 and 375: 374 MOSEK MSK IPAR LOG BI output co
Page 376 and 377: 376 MOSEK MSK IPAR SIM PRIMAL CRASH
Page 378 and 379: 378 MOSEK MSK IPAR PRESOLVE LINDEP
Page 380 and 381: 380 MOSEK MSK DPAR DATA TOL QIJ (re
Page 382 and 383: 382 MOSEK MSK DPAR INTPNT CO TOL NE
Page 384 and 385: 384 MOSEK MSK IPAR INTPNT STARTING
Page 386 and 387: 386 MOSEK MSK IPAR SIM PRIMAL CRASH
Page 388 and 389: 388 MOSEK Coefficient reduction +51
Page 390 and 391: 390 MOSEK 8 The optimizer for nonco
Page 392 and 393: 392 MOSEK Simplex - cpu time: 0.00
Page 394 and 395: 394 MOSEK Presolve - time : 0.00 Pr
Page 396 and 397: 396 MPSWRITE need to use applicatio
Page 398 and 399: 398 MPSWRITE C0000004 X SAN-DIEGO N
Page 400 and 401: 400 MPSWRITE 6 Using Analyze with M
Page 402 and 403: 402 MPSWRITE 7 The GAMS/MPSWRITE Op
Page 404 and 405: 404 MPSWRITE 8 Name Generation Exam
Page 406 and 407: 406 MPSWRITE C CHICAGO T TOPEKA I c
Page 408 and 409: 408 MPSWRITE the macros @i and @j.
Page 410 and 411: 410 MPSWRITE SSA 3 -0.10000000E+21
Page 412 and 413: 412 MPSWRITE < translate .lp file i
Page 414 and 415: 414 MPSWRITE ANALYZE Version 10.0 b
Page 416 and 417: 416 NLPEC gams nash MPEC=nlpec MCP=
Page 418 and 419: 418 NLPEC Note that each inner prod
Page 420 and 421: 420 NLPEC Note that the slack varia
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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
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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 .
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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
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490 PATH 4.6 the generated path ema
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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
Page 510 and 511:
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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