15457293041

Committed 05 Jun 2025 02:50AM UTC coverage: 80.272% (+4.3%) from 75.929%

Build # 15457293041

Build Type

push

github

Committed by

anugrahjo

Commit Message

Fix a failing test

Run Details

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97.37

/modopt/core/optimize.py

from modopt.core.optimization_algorithms.opensqp import OpenSQP
from modopt.external_libraries.scipy import SLSQP, COBYLA, BFGS, LBFGSB, NelderMead, TrustConstr
from modopt.external_libraries.cobyqa import COBYQA
from modopt.external_libraries.pyslsqp import PySLSQP
from modopt.external_libraries.snopt import SNOPTc as SNOPT
from modopt.external_libraries.ipopt import IPOPT
from modopt.external_libraries.cvxopt import CVXOPT
from modopt.external_libraries.qpsolvers import ConvexQPSolvers

def optimize(prob, solver='SLSQP', **kwargs):
    """
    Optimize given problem with the specified solver.

    Only performant algorithms can be used with this function
    and is made available for users who are only interested in
    solving their optimization problems with solvers in the modOpt library.
    Developers of instructional algorithms are recommended to use
    ``Optimizer`` subclasses such as ``SLSQP``, ``Newton``, ``OpenSQP``, etc. directly.

    Parameters
    ----------
    prob : Problem or ProblemLite
        The problem to be solved.
        Needs to be an instance of ``Problem`` or ``ProblemLite``.
    solver : str, default='SLSQP'
        The solver to be used.
        Available solvers are ``'SLSQP'``, ``'PySLSQP'``, ``'COBYLA'``, ``'BFGS'``,
        ``'LBFGSB'``, ``'NelderMead'``, ``'COBYQA'``, ``'TrustConstr'``, ``'OpenSQP'``,
        ``'SNOPT'``, ``'IPOPT'``, ``'CVXOPT'``, and ``'ConvexQPSolvers'``.
    **kwargs
        Additional keyword arguments to be passed to the solver.

    Returns
    -------
    dict
        The results of the optimization.
    """
    valid_solvers = ['SLSQP', 'PySLSQP', 'COBYLA', 'BFGS',
                     'LBFGSB', 'NelderMead', 'COBYQA', 'TrustConstr', 'OpenSQP',
                     'SNOPT', 'IPOPT', 'CVXOPT', 'ConvexQPSolvers']
    if solver == 'SLSQP':
        optimizer = SLSQP(prob, **kwargs)
    elif solver == 'PySLSQP':
        optimizer = PySLSQP(prob, **kwargs)
    elif solver == 'COBYLA':
        optimizer = COBYLA(prob, **kwargs)
    elif solver == 'BFGS':
        optimizer = BFGS(prob, **kwargs)
    elif solver == 'LBFGSB':
        optimizer = LBFGSB(prob, **kwargs)
    elif solver == 'NelderMead':
        optimizer = NelderMead(prob, **kwargs)
    elif solver == 'COBYQA':
        optimizer = COBYQA(prob, **kwargs)
    elif solver == 'TrustConstr':
        optimizer = TrustConstr(prob, **kwargs)
    elif solver == 'OpenSQP':
        optimizer = OpenSQP(prob, **kwargs)
    elif solver == 'SNOPT':
        optimizer = SNOPT(prob, **kwargs)
    elif solver == 'IPOPT':
        optimizer = IPOPT(prob, **kwargs)
    elif solver == 'CVXOPT':
        optimizer = CVXOPT(prob, **kwargs)
    elif solver == 'ConvexQPSolvers':
        optimizer = ConvexQPSolvers(prob, **kwargs)
    else:
        raise ValueError(f"Invalid solver named '{solver}' is specified. Valid solvers are: {valid_solvers}.")

    return optimizer.solve()

1	from modopt.core.optimization_algorithms.opensqp import OpenSQP	1✔
2	from modopt.external_libraries.scipy import SLSQP, COBYLA, BFGS, LBFGSB, NelderMead, TrustConstr	1✔
3	from modopt.external_libraries.cobyqa import COBYQA	1✔
4	from modopt.external_libraries.pyslsqp import PySLSQP	1✔
5	from modopt.external_libraries.snopt import SNOPTc as SNOPT	1✔
6	from modopt.external_libraries.ipopt import IPOPT	1✔
7	from modopt.external_libraries.cvxopt import CVXOPT	1✔
8	from modopt.external_libraries.qpsolvers import ConvexQPSolvers	1✔
9
10	def optimize(prob, solver='SLSQP', **kwargs):	1✔
11	"""
12	Optimize given problem with the specified solver.
13
14	Only performant algorithms can be used with this function
15	and is made available for users who are only interested in
16	solving their optimization problems with solvers in the modOpt library.
17	Developers of instructional algorithms are recommended to use
18	``Optimizer`` subclasses such as ``SLSQP``, ``Newton``, ``OpenSQP``, etc. directly.
19
20	Parameters
21	----------
22	prob : Problem or ProblemLite
23	The problem to be solved.
24	Needs to be an instance of ``Problem`` or ``ProblemLite``.
25	solver : str, default='SLSQP'
26	The solver to be used.
27	Available solvers are ``'SLSQP'``, ``'PySLSQP'``, ``'COBYLA'``, ``'BFGS'``,
28	``'LBFGSB'``, ``'NelderMead'``, ``'COBYQA'``, ``'TrustConstr'``, ``'OpenSQP'``,
29	``'SNOPT'``, ``'IPOPT'``, ``'CVXOPT'``, and ``'ConvexQPSolvers'``.
30	**kwargs
31	Additional keyword arguments to be passed to the solver.
32
33	Returns
34	-------
35	dict
36	The results of the optimization.
37	"""
38	valid_solvers = ['SLSQP', 'PySLSQP', 'COBYLA', 'BFGS',	1✔
39	'LBFGSB', 'NelderMead', 'COBYQA', 'TrustConstr', 'OpenSQP',
40	'SNOPT', 'IPOPT', 'CVXOPT', 'ConvexQPSolvers']
41	if solver == 'SLSQP':	1✔
42	optimizer = SLSQP(prob, **kwargs)	1✔
43	elif solver == 'PySLSQP':	1✔
44	optimizer = PySLSQP(prob, **kwargs)	1✔
45	elif solver == 'COBYLA':	1✔
46	optimizer = COBYLA(prob, **kwargs)	1✔
47	elif solver == 'BFGS':	1✔
48	optimizer = BFGS(prob, **kwargs)	1✔
49	elif solver == 'LBFGSB':	1✔
50	optimizer = LBFGSB(prob, **kwargs)	1✔
51	elif solver == 'NelderMead':	1✔
52	optimizer = NelderMead(prob, **kwargs)	1✔
53	elif solver == 'COBYQA':	1✔
54	optimizer = COBYQA(prob, **kwargs)	1✔
55	elif solver == 'TrustConstr':	1✔
56	optimizer = TrustConstr(prob, **kwargs)	1✔
57	elif solver == 'OpenSQP':	1✔
58	optimizer = OpenSQP(prob, **kwargs)	1✔
59	elif solver == 'SNOPT':	1✔
60	optimizer = SNOPT(prob, **kwargs)	×
61	elif solver == 'IPOPT':	1✔
62	optimizer = IPOPT(prob, **kwargs)	1✔
63	elif solver == 'CVXOPT':	1✔
64	optimizer = CVXOPT(prob, **kwargs)	1✔
65	elif solver == 'ConvexQPSolvers':	1✔
66	optimizer = ConvexQPSolvers(prob, **kwargs)	1✔
67	else:
68	raise ValueError(f"Invalid solver named '{solver}' is specified. Valid solvers are: {valid_solvers}.")	1✔
69
70	return optimizer.solve()	1✔

LSDOlab / modopt / 15457293041

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