Benchmark

The benchmark is adapted from the JuMP paper. Eight optimization models with different sizes are selected as test cases, including four facility location models and four linear quadratic control problems. We conduct two rounds of benchmark using Gurobi and COPT as optimizer respectively. For each model, we measure the total time of modeling interface to generate model and pass it to the optimizer, and the time limit of optimizer is set to 0.0 seconds to avoid the influence of solution process.

All code to run the benchmarks is available at https://github.com/metab0t/PyOptInterface_benchmark.

Time (second) to generate model and pass it to Gurobi optimizer.

Model

Variables

C++

PyOptInterface

JuMP

gurobipy

Pyomo

fac-25

67651

0.2

0.2

0.2

1.2

4.1

fac-50

520301

0.8

1.2

1.8

9.7

32.7

fac-75

1732951

2.7

4.1

6.6

32.5

119.3

fac-100

4080601

6.3

10.0

17.8

79.1

286.3

lqcp-500

251501

0.9

1.5

1.3

6.3

23.8

lqcp-1000

1003001

3.7

6.0

6.1

26.7

106.6

lqcp-1500

2254501

8.3

14.0

17.7

61.8

234.0

lqcp-2000

4006001

14.5

24.9

38.3

106.9

444.1
Time (second) to generate model and pass it to COPT optimizer.

Model

Variables

C++

PyOptInterface

JuMP

coptpy

Pyomo

fac-25

67651

0.3

0.2

0.3

0.6

4.1

fac-50

520301

2.2

1.5

2.7

5.4

32.8

fac-75

1732951

8.1

6.6

10.2

20.3

117.4

fac-100

4080601

22.4

23.4

30.3

58.0

284.0

lqcp-500

251501

3.8

3.1

3.0

6.6

26.4

lqcp-1000

1003001

16.0

15.5

13.9

28.1

112.1

lqcp-1500

2254501

37.6

32.4

33.7

64.6

249.3

lqcp-2000

4006001

68.2

60.3

66.2

118.4

502.4

Recently, there are a lot of requests to test the performance of PyOptInterface compared with linopy and cvxpy, so we prepare a benchmark.

This is the result of benchmark, where the performance of PyOptInterface exceeds linopy and cvxpy significantly.

Time (second) to generate and solve a linear programming model with Gurobi optimizer.

N

Variables

PyOptInterface

linopy

cvxpy

100

20000

0.112849

0.422408

0.373407

200

80000

0.294830

1.118702

1.575949

300

180000

0.710237

2.462809

4.038862

400

320000

1.256276

4.535225

8.687895

500

500000

2.189127

8.243707

18.941519