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gams:min_function_don_t_use_it [2015/09/18 14:34]
Jarungjit Parnjai
gams:min_function_don_t_use_it [2017/09/02 19:23] (current)
support
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 This is possible because we maximize y, which will assume automatically in the optimal solution the minimum of the three right-hand sides. This is possible because we maximize y, which will assume automatically in the optimal solution the minimum of the three right-hand sides.
  
-Chapter [[http://​www.gams.com/​help/topic/gams.doc/​solvers/​conopt/​index.html#​CONOPT_NLP_AND_DNLP_MODELS|NLP and DNLP Models]] of the [[http://​www.gams.com/​help/​topic/​gams.doc/​solvers/​conopt/​index.html|CONOPT Manual]] has some more examples of the reformulation of DNLP models. ​+Chapter [[https://​www.gams.com/​latest/docs/S_CONOPT.html#​CONOPT_NLP_AND_DNLP_MODELS|NLP and DNLP Models]] of the CONOPT Manual has some more examples of the reformulation of DNLP models. ​
  
-There are also several GAMS intrinsic functions that smoothly approximate MIN(f,​g). ​ The motivation for putting these in GAMS was for use in reformulation approaches for MCP and MPEC models - the complementarity conditions can be cast as equations using the MIN function, and the smoothed MIN functions allow solution via NLP solvers. For example, the Fischer-Burmeister function and the Chen-Mangasarian function are both smoothed MIN functions available in GAMS, and since they are intrinsics you only need to write NcpF(f,​g,​mu) or NcpCM(f,​g,​mu),​ where mu is the smoothing parameter (like the delta below). ​ You can get the exact definition for these functions and others that may also help you by looking at section [[http://​www.gams.com/​help/topic/gams.doc/​solvers/​nlpec/​index.html#​NLPEC_NCP_FUNCTIONS|NCP functions]] of the [[http://​www.gams.com/​help/​topic/​gams.doc/​solvers/​nlpec/​index.html|NLPEC solver manual]].+There are also several GAMS intrinsic functions that smoothly approximate MIN(f,​g). ​ The motivation for putting these in GAMS was for use in reformulation approaches for MCP and MPEC models - the complementarity conditions can be cast as equations using the MIN function, and the smoothed MIN functions allow solution via NLP solvers. For example, the Fischer-Burmeister function and the Chen-Mangasarian function are both smoothed MIN functions available in GAMS, and since they are intrinsics you only need to write NcpF(f,​g,​mu) or NcpCM(f,​g,​mu),​ where mu is the smoothing parameter (like the delta below). ​ You can get the exact definition for these functions and others that may also help you by looking at section [[https://​www.gams.com/​latest/docs/S_NLPEC#​NLPEC_NCP_FUNCTIONS|NCP functions]] of the NLPEC solver manual.
  
  
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