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OptimizationProblems.jl problem classification

It is possible to access information on the problems implemented in OptimizationProblems.jl without loading the problems using the package's own classification. ​

using OptimizationProblems

Each problem has its own metadata structure, and there is a global metadata structure regrouping all the information.

Problem's metadata

Each problem's metadata is accessible with OptimizationProblems.nameoftheproblem_meta and regroups in a Dict most of the essential information regarding each problem.

OptimizationProblems.AMPGO02_meta

See ? OptimizationProblems.meta for more documentation on the various entries and their default values.

This structure is completed by getters to access the number of variables, get_nameoftheproblem_nvar, the number of constraints, get_nameoftheproblem_ncon, the number of linear constraints, get_nameoftheproblem_nlin, the number of nonlinear constraints, get_nameoftheproblem_nnln, the number of equality constraints, get_nameoftheproblem_nequ, and the number of inequality constraints, get_nameoftheproblem_nineq.

OptimizationProblems.get_AMPGO02_nvar()

For scalable problems the entry :variable_nvar (and/or :variable_ncon) is set as true and one can access the number of variables by passing the parameters to the getter functions. By default, the number of variables set in the meta is obtained using OptimizationProblems.default_nvar as a parameter to define the problem.

OptimizationProblems.arglina_meta

OptimizationProblems.get_arglina_nvar(n = 10)

OptimizationProblems.PureJuMP.arglina(n = 10)

Global meta

This package collects all the metadata in a single DataFrame.

OptimizationProblems.meta

Then, it is very simple to filter problems using queries on DataFrame. We refer to the documentation of DataFrames.jl for tutorials. For instance, if one wants to select unconstrained scalable problems and use (:nvar, :name).

meta = OptimizationProblems.meta
names_pb_vars = meta[(meta.variable_nvar .== true) .& (meta.ncon .== 0), [:nvar, :name]]

Then, one can prepare a list of problems using the selected ones.

using ADNLPModels
adproblems = (
  eval(Meta.parse("ADNLPProblems.$(pb[:name])()")) for pb in eachrow(names_pb_vars)
)

Nonlinear Least Squares Problems (NLS)

Problems with :objtype set to :least_squares are nonlinear least squares problems (NLS). For these, you can access the number of NLS equations using a getter like get_nameoftheproblem_nls_nequ().

OptimizationProblems.get_lanczos1_nls_nequ()

To filter all NLS problems in the metadata DataFrame:

nls_problems = OptimizationProblems.meta[OptimizationProblems.meta.objtype .== :least_squares, :name]

Test-set membership (:lib)

The :lib column records membership in named optimization test-set collections. Each entry is a comma-separated list of "Collection:ID" pairs; an empty string means the problem has no known test-set membership.

OptimizationProblems.hs1_meta[:lib]  # "CUTEst:HS1, HS:1"

Known collections (keys of OptimizationProblems.LIB_REFERENCES):

Key Description
AMPGO Gavana's Global Optimization benchmark suite
COPS COPS 3.0 collection (Dolan & Moré, 2004)
CUTEst CUTEst testing environment (Gould et al., 2015)
HS Hock & Schittkowski (1981), Vol. 187
HS2 Schittkowski (1987), Vol. 282
Luksan Luksan, Matonoha & Vlček (2003) — Modified CUTE problems
LuksanSparse Luksan, Matonoha & Vlček (2010) — Sparse test problems
MGH Moré, Garbow & Hillstrom (1981)
NIST NIST/ITL Statistical Reference Datasets

To filter by collection, use Julia's contains function:

meta = OptimizationProblems.meta
cops_problems = meta[contains.(meta.lib, "COPS"), [:name, :lib]]

cutest_problems = meta[contains.(meta.lib, "CUTEst"), [:name, :lib]]

OptimizationProblems.LIB_REFERENCES provides the canonical BibTeX entry for each collection, and export_bibtex automatically appends them when include_lib_refs = true (the default).

Problem'source information

The following code will create a .bib file regrouping all the BibTex citations.

using OptimizationProblems
export_bibtex()

Fields documenting the origin of the problem may be incomplete and any help is welcome.