MINLPLibJuMP

Dev:

Most of the existing Mixed-Integer Nonlinear and Nonlinear Programs(MINLP) problems are stored in .gms,.nl,.mod formats designed for commercial modeling languages. The Julia optimization community lacks the support of parsing these inputs into JuMP.jl friendly formats. This is an instance library of MINLP in JuMP model format. With this package, you can have

• Use instances to benchmark solvers and/or perform experiments easily

• View rich meta information of each instance to assist your analyses and experiments

• Build and publish your own reference libraries with target characteristics

Included Libraries

For more details of the libraries included in this package, please see documentation.

Installation

To install, at your Julia command prompt,

Pkg.clone("https://github.com/lanl-ansi/MINLPLibJuMP.jl.git")

Fetching a model

Fetch an instance by its libname/name just like the following,

using MINLPLibJuMP
m = fetch_model("minlp2/blend029")

or

using MINLPLibJuMP
m = fetch_model("minlp2", "blend029")

For special built-in model that requires additional inputs,

using MINLPLibJuMP
m = fetch_model("special", "multiKND", options=Dict(:K=>3, :N=>3, :D=>1))

The above scrip initializes an optimization JuMP model multiKND. The formulation is controlled with parameter :K, :N, and :D, which yields the following problem:

Max x[1] * x[2] * x[3] + x[3] * x[4] * x[5] + x[5] * x[6] * x[7]
Subject to
 x[1] + x[2] + x[3] <= 3
 x[3] + x[4] + x[5] <= 3
 x[5] + x[6] + x[7] <= 3
 0.1 <= x[i] <= 10 for all i in {1,2,..,6,7}

Viewing instance's meta info

To know a instance better, you can do the following to get a dictionary of meta info. Note that as we continue to develop and test. More verified attributes will be added to the meta.

julia> meta = MINLPLibJuMP.fetch_meta("minlp2", "blend029")
Dict{String,Any} with 18 entries:
  "NINTVARS"   => 0
  "NLINCONS"   => 202
  "OBJBOUND"   => nothing
  "NAME"       => "blend029"
  "OBJVAL"     => nothing
  "LIBRARY"    => "minlp2"
  "NCONS"      => 214
  "NVARS"      => 103
  "OBJTYPE"    => "linear"
  "NLOPERATOR" => Any["mul"]
  "LOAD"       => 1.7414
  "OBJSENSE"   => "Max"
  "NSDPCONS"   => 0
  "NNLCONS"    => 12
  "NBINVARS"   => 36
  "NQUADCONS"  => 0
  "NSOSCONS"   => 0

Customize your own instance library

Currently, MINLPLibJuMP.jl contains over 6000 instances. Experimenting with all of them can be computationally heavy and risky. For your research projects, the scope may be limited to a specific type of problems. Hence, it is necessary to construct you very own library for research experiments. Below, we write a small .jl script that will generate a user instance library (without actually copying the problem) called USERLib that collect all instances with exp function. The directory will be created with name instance/USERLib. Please see our PODLib designed for testing Alpine.jl solver. This customized library contains instances reference as well as full instances. Note that each instance can be drastically different with different formulations and implementations (peak into the library called special and you will see some examples). It is up to you to select and construct your own instance library for more exciting and convenient experiments.

using MINLPLibJuMP

# Collect target problem from these libraries
for lib in ["bcp", "global", "ibm", "inf", "minlp", "minlp2"]
    NAMES = MINLPLibJuMP.fetch_names(lib)   # Fetch a list of instance names
    for i in NAMES
        Meta = MINLPLibJuMP.fetch_meta(lib, i)  # Fetch instance meta
        isempty(Meta) && continue
        !haskey(Meta, "NLOPERATOR") && continue
        if "exp" in M["NLOPERATOR"][1]
            MINLPLibJuMP.add_to_lib("USERLib", lib, i)  # Collect instance
        else
            continue
        end
    end
end
println(MINLPLibJuMP.fetch_names("USERLib"))