RAPOSa
RAPOSa is a global optimization solver, specifically designed for mixed-integer polynomial programming problems with box-constrained variables. Written entirely in C++, it is based on the Reformulation-Linearization Technique developed by Hanif D. Sherali and Cihan H. Tuncbilek, and subsequently improved by Hanif D. Sherali, Evrim Dalkiran and collaborators.
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How to use it
ampl: option solver raposa; # change the solver
ampl: option raposa_options 'option1=value1 option2=value2'; # specify options
ampl: solve; # solve the problem
from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="raposa", raposa_options="option1=value1 option2=value2")
Learn more about what we have to offer to implement and deploy Optimization in Python.
AMPL APIs are interfaces that allow developers to access the features of the AMPL interpreter from within a programming language. We have APIs available for:
Options
Full list of solver options: