Source code for cvxpy.reductions.dgp2dcp.dgp2dcp

Copyright 2018 Akshay Agrawal

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import numpy as np

from cvxpy import settings
from cvxpy.reductions.canonicalization import Canonicalization
from cvxpy.reductions.dgp2dcp.atom_canonicalizers import DgpCanonMethods

[docs]class Dgp2Dcp(Canonicalization): """Reduce DGP problems to DCP problems. This reduction takes as input a DGP problem and returns an equivalent DCP problem. Because every (generalized) geometric program is a DGP problem, this reduction can be used to convert geometric programs into convex form. Example ------- >>> import cvxpy as cp >>> >>> x1 = cp.Variable(pos=True) >>> x2 = cp.Variable(pos=True) >>> x3 = cp.Variable(pos=True) >>> >>> monomial = 3.0 * x_1**0.4 * x_2 ** 0.2 * x_3 ** -1.4 >>> posynomial = monomial + 2.0 * x_1 * x_2 >>> dgp_problem = cp.Problem(cp.Minimize(posynomial), [monomial == 4.0]) >>> >>> dcp2cone = cvxpy.reductions.Dcp2Cone() >>> assert not dcp2cone.accepts(dgp_problem) >>> >>> gp2dcp = cvxpy.reductions.Dgp2Dcp(dgp_problem) >>> dcp_problem = gp2dcp.reduce() >>> >>> assert dcp2cone.accepts(dcp_problem) >>> dcp_problem.solve() >>> >>> dgp_problem.unpack(gp2dcp.retrieve(dcp_problem.solution)) >>> print(dgp_problem.value) >>> print(dgp_problem.variables()) """ def __init__(self, problem=None) -> None: # Canonicalization of DGP is stateful; canon_methods created # in `apply`. super(Dgp2Dcp, self).__init__(canon_methods=None, problem=problem)
[docs] def accepts(self, problem): """A problem is accepted if it is DGP. """ return problem.is_dgp() and all( p.value is not None for p in problem.parameters())
[docs] def apply(self, problem): """Converts a DGP problem to a DCP problem. """ if not self.accepts(problem): raise ValueError("The supplied problem is not DGP.") self.canon_methods = DgpCanonMethods() equiv_problem, inverse_data = super(Dgp2Dcp, self).apply(problem) inverse_data._problem = problem return equiv_problem, inverse_data
[docs] def canonicalize_expr(self, expr, args): if type(expr) in self.canon_methods: return self.canon_methods[type(expr)](expr, args) else: return expr.copy(args), []
[docs] def invert(self, solution, inverse_data): solution = super(Dgp2Dcp, self).invert(solution, inverse_data) if solution.status == settings.SOLVER_ERROR: return solution for vid, value in solution.primal_vars.items(): solution.primal_vars[vid] = np.exp(value) # f(x) = e^{F(u)}. solution.opt_val = np.exp(solution.opt_val) return solution