A repository launching benchmarks with RipQP and saving performance profiles and tables to measure performance. The benchmarks use the Netlib problems (LPs), the Maros and Meszaros problems (QPS), and the problems from the article (in quadruple precision):
- D. Ma, L. Yang, R. M. T. Fleming, I. Thiele, B. O. Palsson, and M. A. Saunders, Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression, Scientific Reports, 7(1):40863, Feb. 2017. ISSN 2045-2322.
To launch all benchmarks, you will need:
- CPLEX and CPLEX.jl: follow the instructions at https://github.com/jump-dev/CPLEX.jl
- Gurobi and Gurobi.jl: follow the instructions at https://github.com/jump-dev/Gurobi.jl
- Xpress and Xpress.jl: follow the instructions at https://github.com/jump-dev/Xpress.jl
- HSL.jl: follow the instructions at https://github.com/JuliaSmoothOptimizers/HSL.jl
Then, use
pkg> add https://github.com/geoffroyleconte/RipQPBenchmarks.jl.git
All the benchmarks, profiles and tables can be generated with the function
using RipQPBenchmarks
save_path = "./" # your path to save stats
ripqp_all_benchmarks(
save_path;
run_cplex = false,
run_gurobi = false,
run_xpress = false,
run_ma57 = false,
run_ma97 = false,
plot_extension = ".pdf",
)
save_path
is the directory where the benchmarks (.CSV
files), the profiles, and the tables (saved as .md
and .tex
) will be saved.
The plot_extension
keyword argument used to generate the performance profile has only been tested with ".pdf"
.
Set the keyword arguments run_cplex
, run_gurobi
, etc... according to the installed deps.
This function might take a long time to execute (more than 2 days on a slow computer for me).
To run the benchmarks on Netlib and Maros and Meszaros problems, use
save_path = "./" # your path to save stats
run_benchmarks_solvers(
save_path;
run_cplex = false,
run_gurobi = false,
run_xpress = false,
run_ma57 = false,
run_ma97 = false,
)
To save the performance profiles computed on the Netlib and Maros and Meszaros datasets, use
save_all_profiles(
data_path,
profile_path;
plot_extension = ".pdf",
run_cplex = false,
run_gurobi = false,
run_xpress = false,
run_ma57 = false,
run_ma97 = false,
)
where data_path
is the path containing the results of the benchmarks (save_path
of the previous section) and profile_path
is the path where the profiles should be saved (can be the same as data_path
).
To run the benchmarks on the problems in quadruple precision, use
run_benchmarks_quad(save_path)
To generate the table in quadruple precision, use
quad_prec_table(data_path, table_path)
where data_path
is the path containing the results of the benchmarks (save_path
of the benchmark section)
and table_path
is the path where the tables should be saved.
To generate the table of the smallest residuals that RipQP can reach for the problems in quadruple precision, use
smallest_quad_resid_table(table_path)