Plasmo.jl

Plasmo.jl (Platform for Scalable Modeling and Optimization) is modeling interface that facilitates solving optimization problems using a graph-based approach. The package enables component style modeling by means of modularizing optimization models that can be constructed independently and connected using linking constraints.
The graph-based modeling approach facilitates decomposition techniques such as graph partitioning and community detection which enable the use of parallel solvers such as PIPS-NLP,DSP, or built-in Julia solvers for Benders and Lagrangean Decomposition. Plasmo.jl has been developed by the Scalable Systems Laboratory at the University of Wisconsin-Madison.

Documentation

Documentation is available through GitHub Pages. Additional examples can be found in the examples folder.

Installation

using Pkg
Pkg.add("Plasmo.jl")

Simple Example

Plasmo.jl uses optimization models written with JuMP.

using JuMP
using Plasmo
using Ipopt

graph = ModelGraph()
setsolver(graph,IpoptSolver())

#Add nodes to a ModelGraph
n1 = add_node!(graph)
n2 = add_node!(graph)

#Create JuMP models
m1 = Model()
@variable(m1,0 <= x <= 2)
@variable(m1,0 <= y <= 3)
@constraint(m1,x+y <= 4)
@objective(m1,Min,x)

m2 = Model()
@variable(m2,x)
@NLconstraint(m2,exp(x) >= 2)

#Set JuMP models on nodes
setmodel(n1,m1)     #set m1 to node 1.  Updates reference on m1
setmodel(n2,m2)

#Link constraints take the same expressions as the JuMP @constraint macro
@linkconstraint(graph,n1[:x] == n2[:x])

#Get all of the link constraints in a graph
links = getlinkconstraints(graph)

solve(graph)

println("n1[:x]= ",JuMP.getvalue(n1[:x]))
println("n2[:x]= ",JuMP.getvalue(n2[:x]))

Acknowledgments

This code is based on work supported by the U.S. Department of Energy (DOE), Office of Science, under Contract No. DE-AC02-06CH11357 as well as the DOE Office of Electricity Delivery and Energy Reliability’s Advanced Grid Research and Development program at Argonne National Laboratory.

Citing Plasmo

If you find Plasmo useful for your work, you might cite the current pre-print of the manuscript:

@article{JalvingZavala2018,
archivePrefix = {arXiv},
arxivId = {arXiv:1812.04983v1},
author = {Jalving, Jordan and Cao, Yankai and Zavala, Victor M},
eprint = {arXiv:1812.04983v1},
title = {{Graph-Based Modeling and Simulation of Complex Systems}}
}