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Add benchmark against linopy and cvxpy
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| import time | ||
| from numpy import arange | ||
| import numpy as np | ||
| import pandas as pd | ||
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| from linopy import Model | ||
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| import pyoptinterface as poi | ||
| from pyoptinterface import gurobi | ||
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| import cvxpy | ||
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| def create_linopy_model(N): | ||
| m = Model() | ||
| x = m.add_variables(coords=[arange(N), arange(N)]) | ||
| y = m.add_variables(coords=[arange(N), arange(N)]) | ||
| m.add_constraints(x - y >= arange(N)) | ||
| m.add_constraints(x + y >= 0) | ||
| m.add_objective((2 * x).sum() + y.sum()) | ||
| return m | ||
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| def create_cvxpy_model(N): | ||
| x = cvxpy.Variable((N, N)) | ||
| y = cvxpy.Variable((N, N)) | ||
| constraints = [] | ||
| for i in range(N): | ||
| constraints.append(x[:, i] - y[:, i] >= np.arange(N)) | ||
| constraints.append(x + y >= 0) | ||
| objective = cvxpy.Minimize(2 * cvxpy.sum(x) + cvxpy.sum(y)) | ||
| return cvxpy.Problem(objective, constraints) | ||
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| def create_poi_model(N): | ||
| m = gurobi.Model() | ||
| x = m.add_variables(range(N), range(N)) | ||
| y = m.add_variables(range(N), range(N)) | ||
| for i in range(N): | ||
| for j in range(N): | ||
| m.add_linear_constraint(x[i, j] - y[i, j], poi.Geq, i) | ||
| m.add_linear_constraint(x[i, j] + y[i, j], poi.Geq, 0) | ||
| expr = poi.ExprBuilder() | ||
| poi.quicksum_(expr, x, lambda x: 2 * x) | ||
| poi.quicksum_(expr, y) | ||
| m.set_objective(expr) | ||
| return m | ||
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| def bench(N): | ||
| results = {} | ||
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| t0 = time.time() | ||
| model = create_poi_model(N) | ||
| model.optimize() | ||
| t1 = time.time() | ||
| results["n_variables"] = 2 * N * N | ||
| results["poi"] = t1 - t0 | ||
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| t0 = time.time() | ||
| model = create_linopy_model(N) | ||
| model.solve(solver_name="gurobi", io_api="direct") | ||
| t1 = time.time() | ||
| results["linopy"] = t1 - t0 | ||
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| t0 = time.time() | ||
| model = create_cvxpy_model(N) | ||
| model.solve(solver=cvxpy.GUROBI) | ||
| t1 = time.time() | ||
| results["cvxpy"] = t1 - t0 | ||
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| return results | ||
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| def main(): | ||
| Ns = range(100, 501, 100) | ||
| results = [] | ||
| for N in Ns: | ||
| results.append(bench(N)) | ||
| # create a DataFrame | ||
| df = pd.DataFrame(results, index=Ns) | ||
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| # show result | ||
| print(df) | ||
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| if __name__ == "__main__": | ||
| main() |
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