SDPLib Importer
This helper function reads the problem data specified in SDPA sparse file format, transforms and saves the problem data into the easier to handle Julia Data Format (JLD). The benchmark problems are provided in the SDPLib 1.2 (a library of semidefinite programming test problems) by Brian Borchers. They are availabe for download: http://infohost.nmt.edu/~borchers/sdplib.html.
Prerequisites
- The code is written for Julia v1.0
- You need the
FileIO
andJLD2
package to handle the Julia Data Format (JLD2). To get it simply typeadd ("JLD2")
.
Installing / Using
- Clone the repository to your local machine.
- Add the original SDPLib 1.2 problem files to a folder
/sdplib
(or edit the path inside SDPLib_Importer.jl). - Run the
SDPLib_Importer.jl
file. (This will create .jld2 files inside the /sdplib/ folder.)
Testing
- The following is a short test script that loads a converted problem in
.jld2
format, solves the problem usingJuMP
and theCOSMO.jl
solver:
using FileIO, COSMO, SparseArrays, LinearAlgebra, Test, JuMP
data = load("./sdplib/maxG11.jld2");
F = data["F"]
c = data["c"]
m = data["m"]
n = data["n"]
obj_true = data["optVal"]
# Describe primal problem using JuMP and solve with COSMO Solver
model = JuMP.Model(with_optimizer(COSMO.Optimizer, verbose = true, decompose = true));
@variable(model, x[1:m]);
@objective(model, Min, c' * x);
@constraint(model, con1, Symmetric(-Matrix(F[1]) + sum(Matrix(F[k + 1]) .* x[k] for k in 1:m)) in JuMP.PSDCone());
JuMP.optimize!(model);
Authors
- Michael Garstka
License
This project is licensed under the Apache License - see the LICENSE.md file for details.
Questions / Bugs
- Please use the Issue Tracker to report bugs or to ask questions.