Description

LSE max LSEMax is defined as: f := log(exp(x1)+exp(x2)+...)

Not every GAMS solver can handle this. For those, who cannot deal
with this function, we expect a capability error. For others we
check the evaluation of random input points.

Contributor: Lutz Westermann, September 2022

Small Model of Type : NLP

Category : GAMS Test library

Main file : fnlseslv.gms

$title 'Rough solver correctness test for LSEMax intrinsics' (FNLSESLV,SEQ=917)

$onText
LSE max LSEMax is defined as: f := log(exp(x1)+exp(x2)+...)
  
Not every GAMS solver can handle this. For those, who cannot deal
with this function, we expect a capability error. For others we
check the evaluation of random input points.

Contributor: Lutz Westermann, September 2022
$offText


Sets
*  Allow to fit into global demo limit
   arg / n00*n07 /
   P   / p1*p100 /;
Scalars
   aTol0  / 3e-14 /;
Parameters
   data(arg)
   wantZ;

Variable z, Vdata(arg);
Equation e, fx(arg);

e.. z =e= LSEMax(Vdata('n00'),Vdata('n01'),Vdata('n02'),Vdata('n03'),
                 Vdata('n04'),Vdata('n05'),Vdata('n06'),Vdata('n07') );

fx(arg).. Vdata(arg) =e= data(arg);

model m /all/;

loop {P,
  data(arg) = uniform(-10,10);
  solve m min z use nlp;
  if {(m.solvestat = %solveStat.capabilityProblems%),
    abort$(m.modelstat <> %modelStat.noSolutionReturned%) 'wrong modelstat for capability error';
    abort.noError                                         'Solver capability error: further tests suppressed';
  else
    abort$(m.solvestat <> %solveStat.normalCompletion% or m.modelstat > %modelStat.locallyOptimal%) 'wrong status codes';
    wantZ = log(sum(arg, exp(data(arg))));
    abort$[abs(z.l-wantZ)>aTol0] 'bad z.l', z.l, wantZ;
  };
};