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408 MPSWRITE the macros @i and @j. In the string, ANALYZE recognizes the characters between ’&’ and ’:’ as representing the stem of the index name corresponding to the string following the ’@’ macro in the gams file, and the number immediately following the ’:’ as the number of characters from the left of the row/column name that the particular set label stem begins. As an illustration, in the row syntax for X, we see the strings &I.:2 and &J.:4. This means that in any row of X, say XSENE, the label corresponding to I begins at the second position (SE) while the label corresponding to J begins at the fourth position (NE). This is used by the ANALYZE command EXPLAIN. For further information on this topic, consult the ANALYZE manual. The ANALYZE LP file shown in the ”ANALYZE LP file with FIXED=1” demonstrates the effect of the option fixed which uses a fixed field format for the LP file. This makes the file size larger but at the same time it is easier to read. Modified ’trnsport.gms’ \$TITLE A TRANSPORTATION PROBLEM (TRNSPORT,SEQ=1) \$OFFUPPER SETS I canning plants / SEATTLE, SAN-DIEGO / J markets / NEW-YORK, CHICAGO, TOPEKA / ; PARAMETERS A(I) capacity of plant i in cases / SEATTLE 350 SAN-DIEGO 600 / B(J) demand at market j in cases / NEW-YORK 325 CHICAGO 300 TOPEKA 275 / ; TABLE D(I,J) distance in thousands of miles NEW-YORK CHICAGO TOPEKA SEATTLE 2.5 1.7 1.8 SAN-DIEGO 2.5 1.8 1.4 ; SCALAR F freight in dollars per case per thousand miles /90/ ; PARAMETER C(I,J) transport cost in thousands of dollars per case ; C(I,J) = F * D(I,J) / 1000 ; VARIABLES X(I,J) shipment quantities in cases from @i to @j Z total transportation costs in thousands of dollars ; POSITIVE VARIABLE X ; EQUATIONS COST define objective function SUPPLY(I) observe supply limit at plant @i DEMAND(J) satisfy demand at market @j ; COST .. Z =E= SUM((I,J), C(I,J)*X(I,J)) ;
MPSWRITE 409 SUPPLY(I) .. SUM(J, X(I,J)) =L= A(I) ; DEMAND(J) .. SUM(I, X(I,J)) =G= B(J) ; MODEL TRANSPORT /ALL/ ; OPTION LP = MPSWRITE ; SOLVE TRANSPORT USING LP MINIMIZING Z ; DISPLAY X.L, X.M ; ANALYZE Syntax file with TEXTOPT=1 * SYNTAX FILE GENERATED BY GAMS/MPSWRITE 00 Universal Set SE SEATTLE SA SAN-DIEGO NE NEW-YORK CH CHICAGO TO TOPEKA I. canning plants SE SEATTLE SA SAN-DIEGO J. markets NE NEW-YORK CH CHICAGO TO TOPEKA ROW SYNTAX C define objective function S observe supply limit at plant &I.:2 D satisfy demand at market &J.:2 COLUMN SYNTAX X shipment quantities in cases from &I.:2 to &J.:4 Z total transportation costs in thousands of dollars ENDATA ANALYZE LP file with FIXED=1 * file created by gams * BASE OPTIMAL = MINIMIZE STATUS = UNKNOWN NAME = GAMS ROWS = 7 COLUMNS = 7 NONZEROS = 20 LENGTH = 8 OBJECTIV = +OBJ ENDBASE *** ROWS C 7 0.00000000E+00 0.00000000E+00 B 0.00000000E+00 0.00000000E+00 SSE 3 -0.10000000E+21 0.35000000E+03 B 0.00000000E+00 0.00000000E+00
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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
Page 50 and 51:
50 COIN-OR This sets the algorithm
Page 52 and 53:
52 COIN-OR Option type default B-BB
Page 54 and 55:
54 COIN-OR integer tolerance: Set i
Page 56 and 57:
56 COIN-OR stop diving on cutoff: F
Page 58 and 59:
58 COIN-OR userjobid: Postfixes gdx
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60 COIN-OR nlp solve max depth: Set
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62 COIN-OR maxmin crit no sol: Weig
Page 64 and 65:
64 COIN-OR General Options iterlim
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66 COIN-OR This option causes GAMS
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68 COIN-OR scaling (string) Scaling
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70 COIN-OR sos This option let CBC
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72 COIN-OR knapsackcuts (string) De
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74 COIN-OR 1 Turns the feasibility
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76 COIN-OR userheurmult (integer) D
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78 COIN-OR This option determines t
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80 COIN-OR noiterlim (integer) Allo
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82 COIN-OR linear_solver pardiso pa
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84 COIN-OR # This is a comment # Tu
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86 COIN-OR acceptable tol: ”Accep
Page 88 and 89:
88 COIN-OR slack bound push: Desire
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90 COIN-OR linear system scaling: M
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92 COIN-OR mumps pivtol: Pivot tole
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94 COIN-OR fixed variable treatment
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96 COIN-OR adaptive mu monotone ini
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98 COIN-OR quality function balanci
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100 COIN-OR alpha min frac: Safety
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102 COIN-OR • no: don’t skip
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104 COIN-OR max resto iter: Maximum
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106 COIN-OR • no: perturbation on
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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
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 372 and 373: 372 MOSEK The first option specifie
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 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 444 and 445: 444 OSL sets the amount of memory u
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
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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
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
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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
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
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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
Page 512 and 513:
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 --
Page 546 and 547:
546 SNOPT Merit is the value of the
Page 548 and 549:
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
Page 558 and 559:
558 XA Each XA B&B strategy has man
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560 XA Option Description Default D
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562 XA Option Description Default S
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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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Page 574:
574 XPRESS
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