GAMS — The Solver Manuals - Available Software

10 AlphaECP
1.2 Running GAMS/AlphaECP
GAMS/AlphaECP solves MINLP models. If GAMS/AlphaECP is not specified as the default solver for these
models, it can be invoked by issuing the following command before the solve statement:
option minlp=alphaecp, miqcp=alphaecp;
In principle, GAMS/AlphaECP can also handle NLP models, but is more suitable for MINLP problems. Especially
in combination with an NLP solver it can find solutions the NLP solver by itself does not find. In this case it
acts as a good starting point generator. If you want to solve NLPs with GAMS/AlphaECP you need to trick the
GAMS system by solving your NLP as an MINLP:
solve mynlpmodel minimizing obj using minlp;
2 GAMS/AlphaECP Output
The log output below is obtained for the MINLP model fuel.gms from the GAMS model library:
AlphaECP
ALFA 14Jun06 WIN.00.01 22.3 004.035.041.VIS
-------------------------------------------------------------------------------
Welcome to Alpha-ECP v1.63
MINLP Problem Solver using the Extended Cutting Plane Approach.
Method development - T.Westerlund, Abo Akademi University, FIN
Algorithm implementation - T.Lastusilta, Abo Akademi University, FIN
Westerlund Tapio and Poern Ray (2002). Optimization & Engineering, 3, 253-280
-------------------------------------------------------------------------------
Minimization problem: "C:\AlphaECP_project\v63b\fuel.gms"
has 4 non-linear constraints and 16 variables
-------------------------------------------------------------------------------
Using following settings
Time limit for AlphaECP (in seconds)
reslim=1000
User specified startpoint (0-2)
startpoint=1
Fraction of possible cuts per iteration (0->0.99) nrcuts=0.25
Increasing pace of cutting planes (0.1->1)
cutgrowth=0.95
Interval for checking cutgrowth
checkgrowth=11
Lower limit for cutgrowth option when reducing it autogrowth=0.60
Minimum number of cuts per constraint before reduction mincuts=1
Cutting plane deletion criteria (0-3)
delcrit=3
Interval for checking if violation is reducing
checkviol=13
Return solution (1.MIP/2.NLP/3.QUALITY/4.PERFORMANCE) retsol=2
Level of AlphaECP output to statusfile (0-4)
ECPloglevel=0
AlphaECP strategy (1-5)
strategy=2
Minimum step size for a new linearization
epsilon_z=0.1
Updating multiplier if MIP is infeasible
ECPbeta=1.3
Updating multiplier when verifying solution
ECPgamma=2
Constraint tolerance
epsilon_g=0.001
Gradient tolerance
gradtol=1e-006
Infinity bound (MIP variable bound)
infbnd=1e+010
Maximum number of AlphaECP iterations
ECPiterlim=0
MIP solver for subproblems
MIPsolver=CPLEX
Relative mip gap in intermediate subproblems (0->1.0) MIPoptcr=1.00
Reduce MIPoptcr at iteration (next:limitx2)
MIPoptcrlim=200
NLP solver. Inactive:0 Active strategy:1-5
callnlp=5