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174 CPLEX 11 Start. The number of times that GAMS/CPLEX applies the heuristic is controlled by the repair tries parameter (RepairTries). If this process succeeds, the solution will be treated as an integer solution of the current problem. A MIP start will only be used by GAMS/CPLEX if the MipStart parameter is set to 1. 6.7 Using the Feasibility Relaxation The feasibility relaxation is enabled by the FeasOpt parameter in a CPLEX solver option file. With the FeasOpt option CPLEX accepts an infeasible model and selectively relaxes the bounds and constraints in a way that minimizes a weighted penalty function. In essence, the feasible relaxation tries to suggest the least change that would achieve feasibility. It returns an infeasible solution to GAMS and marks the relaxations of bounds and constraints with the INFES marker in the solution section of the listing file. By default all equations are candiates for relaxation and weigthed equally but none of the variables can be relaxed. This default behavior can be modified by assigning relaxation preferences to variable bounds and constraints. These preferences can be conveniently specified with the .feaspref option. A negative or zero preference means that the associated bound or constraint is not to be modified. The weighted penalty function is constructed from these preferences. The larger the preference, the more likely it will be that a given bound or constraint will be relaxed. However, it is not necessary to specify a unique preference for each bound or range. In fact, it is conventional to use only the values 0 (zero) and 1 (one) except when your knowledge of the problem suggests assigning explicit preferences. Preferences can be specified through a CPLEX solver option file. The syntax is: (variable or equation).feaspref (value) For example, suppose we have a GAMS declaration: Set i /i1*i5/; Set j /j2*j4/; variable v(i,j); equation e(i,j); Then, the relaxation preference in the cplex.opt file can be specified by: feasopt 1 v.feaspref 1 v.feaspref(’i1’,*) 2 v.feaspref(’i1’,’j2’) 0 e.feaspref(*,’j1’) 0 e.feaspref(’i5’,’j4’) 2 First we turn the feasible relaxtion on. Futhermore, we specify that all variables v(i,j) have preference of 1, except variables over set element i1, which have a preference of 2. The variable over set element i1 and j2 has preference 0. Note that preferences are assigned in a procedural fashion so that preferences assigned later overwrite previous preferences. The same syntax applies for assigning preferences to equations as demonstrated above. If you want to assign a preference to all variables or equations in a model, use the keywords variables or equations instead of the individual variable and equations names (e.g. variables.feaspref 1). The parameter FeasOptMode allows different strategies in finding feasible relaxation in one or two phases. In its first phase, it attempts to minimize its relaxation of the infeasible model. That is, it attempts to find a feasible solution that requires minimal change. In its second phase, it finds an optimal solution (using the original objective) among those that require only as much relaxation as it found necessary in the first phase. Values of the parameter FeasOptMode indicate two aspects: (1) whether to stop in phase one or continue to phase two and (2) how to measure the relaxation (as a sum of required relaxations; as the number of constraints and bounds required to be relaxed; as a sum of the squares of required relaxations). Please check description of parameter FeasOpt FeasOptMode for details. Also check example models feasopt* in the GAMS Model library.
CPLEX 11 175 7 GAMS/Cplex Log File Cplex reports its progress by writing to the GAMS log file as the problem solves. Normally the GAMS log file is directed to the computer screen. The log file shows statistics about the presolve and continues with an iteration log. For the primal simplex algorithm, the iteration log starts with the iteration number followed by the scaled infeasibility value. Once feasibility has been attained, the objective function value is listed instead. At the default value for option simdisplay there is a log line for each refactorization. The screen log has the following appearance: Tried aggregator 1 time. LP Presolve eliminated 2 rows and 39 columns. Aggregator did 30 substitutions. Reduced LP has 243 rows, 335 columns, and 3912 nonzeros. Presolve time = 0.01 sec. Using conservative initial basis. Iteration log . . . Iteration: 1 Scaled infeas = 193998.067174 Iteration: 29 Objective = -3484.286415 Switched to devex. Iteration: 98 Objective = -1852.931117 Iteration: 166 Objective = -349.706562 Optimal solution found. Objective : 901.161538 The iteration log for the dual simplex algorithm is similar, but the dual infeasibility and dual objective are reported instead of the corresponding primal values: Tried aggregator 1 time. LP Presolve eliminated 2 rows and 39 columns. Aggregator did 30 substitutions. Reduced LP has 243 rows, 335 columns, and 3912 nonzeros. Presolve time = 0.01 sec. Iteration log . . . Iteration: 1 Scaled dual infeas = 3.890823 Iteration: 53 Dual objective = 4844.392441 Iteration: 114 Dual objective = 1794.360714 Iteration: 176 Dual objective = 1120.183325 Iteration: 238 Dual objective = 915.143030 Removing shift (1). Optimal solution found. Objective : 901.161538 The log for the network algorithm adds statistics about the extracted network and a log of the network iterations. The optimization is finished by one of the simplex algorithms and an iteration log for that is produced as well. Tried aggregator 1 time. LP Presolve eliminated 2 rows and 39 columns.
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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
Page 124 and 125: 124 CONOPT of 4.1**2, which means t
Page 126 and 127: 126 CONOPT X.SCALE(I,J,K)$IJK(I,J,K
Page 128 and 129: 128 CONOPT delta. The error is redu
Page 130 and 131: 130 CONOPT the NLP solver will comp
Page 132 and 133: 132 CONOPT The relationship between
Page 134 and 135: 134 CONOPT A : ❅ ❅ ❅ ❅ Zero
Page 136 and 137: 136 CONOPT EQUATION E1, E2, E3, E4;
Page 138 and 139: 138 CONOPT unfortunate that a redun
Page 140 and 141: 140 CONOPT The Crash procedure is n
Page 142 and 143: 142 CONOPT infeasible equations (th
Page 144 and 145: 144 CONOPT The steepest edge proced
Page 146 and 147: 146 CONOPT CONOPT will after the pr
Page 148 and 149: 148 CONOPT X2 appearing in E2: Pivo
Page 150 and 151: 150 CONOPT Until a final solution h
Page 152 and 153: 152 CONOPT Option Description Defau
Page 154 and 155: 154 CONOPT Option Description Defau
Page 156 and 157: 156 CONOPT
Page 158 and 159: 158 CONVERT • LindoMPI • LINGO
Page 160 and 161: 160 CONVERT Option Description Defa
Page 162 and 163: 162 CPLEX 11 2 How to Run a Model w
Page 164 and 165: 164 CPLEX 11 • You can collect al
Page 166 and 167: 166 CPLEX 11 individual GDX solutio
Page 168 and 169: 168 CPLEX 11 parallelmode predual p
Page 170 and 171: 170 CPLEX 11 mipordind mipordtype m
Page 172 and 173: 172 CPLEX 11 6 Special Notes 6.1 Ph
Page 176 and 177: 176 CPLEX 11 Aggregator did 30 subs
Page 178 and 179: 178 CPLEX 11 Nodes Cuts/ Node Left
Page 180 and 181: 180 CPLEX 11 baritlim (integer) Det
Page 182 and 183: 182 CPLEX 11 -1 Do not generate cov
Page 184 and 185: 184 CPLEX 11 .divflt (real) A diver
Page 186 and 187: 186 CPLEX 11 feasoptmode (integer)
Page 188 and 189: 188 CPLEX 11 implbd (integer) Deter
Page 190 and 191: 190 CPLEX 11 wasted computation tim
Page 192 and 193: 192 CPLEX 11 netppriind (integer) N
Page 194 and 195: 194 CPLEX 11 Settings of this paral
Page 196 and 197: 196 CPLEX 11 1 Force node presolve
Page 198 and 199: 198 CPLEX 11 repairtries (integer)
Page 200 and 201: 200 CPLEX 11 simdisplay (integer) T
Page 202 and 203: 202 CPLEX 11 populate procedure aga
Page 204 and 205: 204 CPLEX 11 1 Display standard min
Page 206 and 207: 206 CPLEX 11
Page 208 and 209: 208 DECIS 1 DECIS 1.1 Introduction
Page 210 and 211: 210 DECIS 1.4 Solving the Universe
Page 212 and 213: 212 DECIS SMPS (stochastic mathemat
Page 214 and 215: 214 DECIS statement would work as w
Page 216 and 217: 216 DECIS RHS demand m 1000.00 peri
Page 218 and 219: 218 DECIS omega1 defines all realiz
Page 220 and 221: 220 DECIS nzrows — Number of rows
Page 222 and 223: 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
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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
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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
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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
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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
Page 490 and 491:
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
Page 504 and 505:
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
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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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