def infer_domain(f, gts, eqs, check_feas=True):
"""
Identify a subset of the constraints in ``gts`` and ``eqs`` which can be incorporated into
conditional SAGE relaxations for signomials. Construct a SigDomain object from the inferred constraints.
Parameters
----------
f : Signomial
The objective in a desired SAGE relaxation. This parameter is only used to determine
the dimension of the set defined by constraints in ``gts`` and ``eqs``.
gts : list of Signomials
For every ``g in gts``, there is a desired constraint that variables ``x`` satisfy ``g(x) >= 0``.
eqs : list of Signomials
For every ``g in eqs``, there is a desired constraint that variables ``x`` satisfy ``g(x) == 0``.
check_feas : bool
Indicates whether or not to verify that the returned SigDomain is nonempty.
Returns
-------
X : SigDomain or None
"""
conv_gt = con_gen.valid_posynomial_inequalities(gts)
conv_eqs = con_gen.valid_monomial_equations(eqs)
cl_cons = con_gen.clcons_from_standard_gprep(f.n, conv_gt, conv_eqs)
if len(cl_cons) > 0:
sigdom = SigDomain(f.n,
coniclifts_cons=cl_cons,
gts=conv_gt,
eqs=conv_eqs,
check_feas=check_feas)
return sigdom
else:
return None
def infer_domain(f, gts, eqs, check_feas=True):
"""
Identify a subset of the constraints in ``gts`` and ``eqs`` which can be incorporated into
conditional SAGE relaxations for polynomials. Construct a PolyDomain object from the inferred
constraints.
Parameters
----------
f : Polynomial
The objective in a desired optimization problem. This parameter is only used to determine
the dimension of the set defined by constraints in ``gts`` and ``eqs``.
gts : list of Polynomials
For every ``g in gts``, there is a desired constraint that variables ``x`` satisfy ``g(x) >= 0``.
eqs : list of Polynomials
For every ``g in eqs``, there is a desired constraint that variables ``x`` satisfy ``g(x) == 0``.
check_feas : bool
Indicates whether or not to verify that the returned PolyDomain is nonempty.
Returns
-------
X : PolyDomain or None
"""
# GP-representable inequality constraints (recast as "Signomial >= 0")
gp_gts = con_gen.valid_gp_representable_poly_inequalities(gts)
gp_gts_sigreps = [Signomial(g.alpha, g.c) for g in gp_gts]
gp_gts_sigreps = con_gen.valid_posynomial_inequalities(gp_gts_sigreps)
# ^ That second call is to convexify the signomials.
# GP-representable equality constraints (recast as "Signomial == 0")
gp_eqs = con_gen.valid_gp_representable_poly_eqs(eqs)
gp_eqs_sigreps = [Signomial(g.alpha, g.c) for g in gp_eqs]
gp_eqs_sigreps = con_gen.valid_monomial_equations(gp_eqs_sigreps)
# ^ That second call is to make sure the nonconstant term has
# a particular sign (specifically, a negative sign).
clcons = con_gen.clcons_from_standard_gprep(f.n, gp_gts_sigreps,
gp_eqs_sigreps)
if len(clcons) > 0:
polydom = PolyDomain(f.n,
logspace_cons=clcons,
gts=gp_gts,
eqs=gp_eqs,
check_feas=check_feas)
return polydom
else:
return None