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Enabling the use of gurobipy directly, which should be faster #60

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Enabling the use of gurobipy directly, which should be faster #60

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41 changes: 39 additions & 2 deletions polytope/solvers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,9 +50,17 @@
except ImportError:
logger.info('MOSEK solver not found.')

try:
import gurobipy as gp
installed_solvers.add('gurobi')
except ImportError:
logger.info('GUROBI solver not found')


# choose default from installed choices
if 'glpk' in installed_solvers:
if 'gurobi' in installed_solvers:
default_solver = 'gurobi'
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I lean towards not making gurobi a default solver, because gurobi and gurobipy are proprietary, closed-source software: https://www.gurobi.com/downloads/end-user-license-agreement-academic/, and: https://pypi.org/project/gurobipy/.

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I agree with not making gurobi a default solver. This is not a vote against Gurobi, but rather, automatically using a restrictive-license-encumbered dependency is an anti-pattern of open source code. Users should have to explicitly request it.

Perhaps there is a more convenient way for users to do this, e.g., an environment variable.

elif 'glpk' in installed_solvers:
default_solver = 'glpk'
elif 'scipy' in installed_solvers:
default_solver = 'scipy'
Expand All @@ -62,7 +70,6 @@
"`installed_solvers` wasn't empty above?")



def lpsolve(c, G, h, solver=None):
"""Try to solve linear program with given or default solver.

Expand All @@ -88,6 +95,8 @@ def lpsolve(c, G, h, solver=None):
result = _solve_lp_using_cvxopt(c, G, h, solver=solver)
elif solver == 'scipy':
result = _solve_lp_using_scipy(c, G, h)
elif solver == 'gurobi':
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Passing the selection 'gurobi' as a keyword argument as done here is explicit [PEP 8] and seems preferable.

result = _solve_lp_using_gurobi(c, G, h)
else:
raise Exception(
'unknown LP solver "{s}".'.format(s=solver))
Expand Down Expand Up @@ -145,6 +154,34 @@ def _solve_lp_using_scipy(c, G, h):
fun=sol.fun)


def _solve_lp_using_gurobi(c, G, h):
"""Attempt linear optimization using gurobipy"""
_assert_have_solver('gurobi')
m = gp.Model()
x = m.addMVar(G.shape[1], lb=-gp.GRB.INFINITY)
m.addConstr(G@x <= h)
m.setObjective(c@x)
m.optimize()

result = dict()
if m.Status == gp.GRB.OPTIMAL:
result['status'] = 0
result['x'] = x.x
result['fun'] = m.ObjVal
return result
elif m.Status == gp.GRB.INFEASIBLE:
result['status'] = 2
elif m.Status == gp.GRB.INF_OR_UNBD or m.Status == gp.GRB.UNBOUNDED:
result['status'] = 3
else:
raise ValueError(f'`gurobipy` returned unexpected status value: {m.Status}')

result['x'] = None
result['fun'] = None
return result



def _assert_have_solver(solver):
"""Raise `RuntimeError` if `solver` is absent."""
if solver in installed_solvers:
Expand Down
2 changes: 1 addition & 1 deletion requirements/extras.txt
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@@ -1 +1 @@
cvxopt==1.2.4
cvxopt==1.2.4
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This line appears to have only whitespace changes. It seems that the line could be omitted from the changeset.

9 changes: 9 additions & 0 deletions tests/polytope_test.py
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Expand Up @@ -486,5 +486,14 @@ def is_glpk_present():
return False


def test_gurobipy_return_same_result_as_scipy():
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It is good to test the added functionality. However, the absence of the solver gurobi on Travis CI implies that this test fails. Skimming the licensing webpage of gurobi gives the impression that installing gurobi on Travis CI is probably far from its licensing procedure. So it seems that this test would need to not be invoked on Travis CI.

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Today I learned that Gurobi has a limited license that is included by default when installing gurobipy, so it is possible now to run tests with Gurobi in CI without requesting special permission1, assuming the model is not "too large" 2.

Footnotes

  1. https://support.gurobi.com/hc/en-us/articles/360044290292-How-do-I-install-Gurobi-for-Python#h_01GRC369WKBMV6VFRCGJPRTSW5 ↩

  2. https://support.gurobi.com/hc/en-us/articles/360051597492-How-do-I-resolve-a-Model-too-large-for-size-limited-Gurobi-license-error ↩

c, A, b = example_1d()
result_gurobi = solvers.lpsolve(c, A, b, solver='gurobi')
result_scipy = solvers.lpsolve(c, A, b, solver='scipy')
nt.assert_equal(result_scipy['status'], result_gurobi['status'])
nt.assert_almost_equal(result_scipy['x'][0], result_gurobi['x'][0])
nt.assert_almost_equal(result_scipy['fun'], result_gurobi['fun'])


if __name__ == '__main__':
pass