#MIGP.jl
A Julia package that uses GPkit to solve Mixed-Integer Geometric Programs.
Usage: Problems are formulated as GPkit-compatible optimization problems (see evtol.py and mico_test.py for examples). Integer varables are declared by adding "INT_" to the variable name in the docstring declaration. For example, declaring "INT_myvar" tells MIGP.jl to treat myvar as an integer variable.
Example: using PyCall @pyimport my_gp_model
import MIGP solve_MIGP(my_gp_model.run)
run is a python function that does the translation between the gp model and Julia. solve_MIGP returns the optimal solution to all integer variables + continuous variables, or an error if there is no feasible integer solution.
Currently, integer variable declarations must be in the top level model.
Julia Packages: LightGraphs, TikzGraphs, TikzPictures, PyCall, JuMP, Mosek, Distributed
Python Packages: gpkit, gpfit, gplibrary, mosek/cvxopt
Installation instructions for gpkit and Mosek can be found here: https://gpkit.readthedocs.io/en/latest/
This package is solver agnostic; Mosek requires a (free academic) license. cvxopt is open source and will also work (albeit more slowly).
gpfit and gplibrary are also required, and can be found at https://github.com/convexengineering.