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February 2015

CUTEst.jl: Julia's CUTEst Interface

This package provides an interface to CUTEst, a repository of constrained and unconstrained nonlinear programming problems for testing and comparing optimization algorithms, derived from the abstract model on NLPModels.

Stable release Github release DOI

  • Documentation: Documentation
  • Chat: Gitter

Development version

  • Documentation: Documentation
  • Tests: Build Status Coverage Status

How to Cite

If you use CUTEst.jl in your work, please cite using the format given in CITATION.bib.

Installing

This package will install CUTEst binaries for your platform automatically. The gfortran compiler is still required to compile decoded SIF problems. No other Fortran compiler is supported.

The following command installs the CUTEst binaries and Julia interface:

pkg> add CUTEst

If you already have a collection of SIF problems that you wish to use, you can simply set the MASTSIF environment variable to point to their location.

Usage

After installing, you can create instances of NLPModels models, with the name CUTEstModel:

using CUTEst

nlp = CUTEstModel("BYRDSPHR");
print(nlp);

This model accepts the same functions as the other NLPModels, for instance

fx = obj(nlp, nlp.meta.x0)
gx = grad(nlp, nlp.meta.x0)
Hx = hess(nlp, nlp.meta.x0)

Run multiple models in parallel

First, decode each of the problems in serial.

function decodemodel(name)
    finalize(CUTEstModel(name))
end

probs = ["AKIVA", "ALLINITU", "ARGLINA", "ARGLINB", "ARGLINC","ARGTRIGLS", "ARWHEAD"]
broadcast(decodemodel, probs)

Then, call functions handling models in parallel. It is important to pass decode=false to CUTEstModel.

addprocs(2)
@everywhere using CUTEst
@everywhere function evalmodel(name)
   nlp = CUTEstModel(name; decode=false)
   retval = obj(nlp, nlp.meta.x0)
   finalize(nlp)
   retval
end

fvals = pmap(evalmodel, probs)

Related Packages

GPLv3

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