def __pow__(self, power, modulo=None):
if self.c.dtype not in __NUMERIC_TYPES__:
raise RuntimeError(
'Cannot exponentiate polynomials with symbolic coefficients.')
temp = Signomial(self.alpha, self.c)
temp = temp**power
temp = temp.as_polynomial()
return temp
def grad(self):
"""
A numpy ndarray of shape ``(n,)`` whose entries are Polynomials. For a numpy ndarray ``x``,
``grad[i](x)`` is the partial derivative of this Polynomial with respect to coordinate ``i``,
evaluated at ``x``. This array is constructed only when necessary, and is cached upon construction.
"""
Signomial._cache_grad(
self) # This ends up calling the Polynomial "_partial" function.
return self._grad
def hess(self):
"""
A numpy ndarray of shape ``(n, n)``, whose entries are Polynomials. For a numpy ndarray ``x``,
``hess[i,j](x)`` is the (i,j)-th partial derivative of this Polynomial, evaluated at ``x``.
This array is constructed only when necessary, and is cached upon construction.
"""
Signomial._cache_hess(
self) # This ends up calling the Polynomial "_partial" function.
return self._hess
def __init__(self, alpha, c):
Signomial.__init__(self, alpha, c)
if not np.all(self.alpha % 1 == 0): # pragma: no cover
raise ValueError('Exponents must be integers.')
if not np.all(self.alpha >= 0): # pragma: no cover
raise ValueError('Exponents must be nonnegative.')
self._sig_rep = None
self._sig_rep_constrs = []
pass
def test_addition_and_subtraction(self):
# data for tests
s0 = Signomial.from_dict({(0, ): 1, (1, ): 2, (2, ): 3})
t0 = Signomial.from_dict({(-1, ): 5})
# tests
s = s0 - s0
s = s.without_zeros()
assert s.m == 1 and set(s.c) == {0}
s = -s0 + s0
s = s.without_zeros()
assert s.m == 1 and set(s.c) == {0}
s = s0 + t0
assert s.alpha_c == {(-1, ): 5, (0, ): 1, (1, ): 2, (2, ): 3}
def test_construction(self):
# data for tests
alpha = np.array([[0], [1], [2]])
c = np.array([1, -1, -2])
alpha_c = {(0, ): 1, (1, ): -1, (2, ): -2}
# Construction with two numpy arrays as arguments
s = Signomial(alpha, c)
assert s.n == 1 and s.m == 3 and s.alpha_c == alpha_c
# Construction with a vector-to-coefficient dictionary
s = Signomial.from_dict(alpha_c)
recovered_alpha_c = dict()
for i in range(s.m):
recovered_alpha_c[tuple(s.alpha[i, :])] = s.c[i]
assert s.n == 1 and s.m == 3 and alpha_c == recovered_alpha_c
def test_signomial_multiplication(self):
# data for tests
s0 = Signomial.from_dict({(0, ): 1, (1, ): 2, (2, ): 3})
t0 = Signomial.from_dict({(-1, ): 1})
q0 = Signomial.from_dict({(5, ): 0})
# tests
s = s0 * t0
s = s.without_zeros()
assert s.alpha_c == {(-1, ): 1, (0, ): 2, (1, ): 3}
s = t0 * s0
s = s.without_zeros()
assert s.alpha_c == {(-1, ): 1, (0, ): 2, (1, ): 3}
s = s0 * q0
s = s.without_zeros()
assert s.alpha_c == {(0, ): 0}
def __add__(self, other):
if isinstance(other, Signomial) and not isinstance(
other, Polynomial): # pragma: no cover
raise RuntimeError('Cannot add signomials to polynomials.')
temp = Signomial.__add__(self, other)
temp = temp.as_polynomial()
return temp
def sig_dual(f, ell=0, X=None, modulator_support=None):
f = f.without_zeros()
# Signomial definitions (for the objective).
lagrangian = f - cl.Variable(name='gamma')
if modulator_support is None:
modulator_support = lagrangian.alpha
t_mul = Signomial(modulator_support,
np.ones(modulator_support.shape[0]))**ell
metadata = {'f': f, 'lagrangian': lagrangian, 'modulator': t_mul, 'X': X}
lagrangian = lagrangian * t_mul
f_mod = f * t_mul
# C_SAGE^STAR (v must belong to the set defined by these constraints).
v = cl.Variable(shape=(lagrangian.m, 1), name='v')
con = relative_dual_sage_cone(lagrangian,
v,
name='Lagrangian SAGE dual constraint',
X=X)
constraints = [con]
# Equality constraint (for the Lagrangian to be bounded).
a = sym_corr.relative_coeff_vector(t_mul, lagrangian.alpha)
a = a.reshape(a.size, 1)
constraints.append(a.T @ v == 1)
# Objective definition and problem creation.
obj_vec = sym_corr.relative_coeff_vector(f_mod, lagrangian.alpha)
obj = obj_vec.T @ v
# Create coniclifts Problem
prob = cl.Problem(cl.MIN, obj, constraints)
prob.metadata = metadata
cl.clear_variable_indices()
return prob
def test_signomial_shift_coordinates(self):
f = Signomial.from_dict({(0,): 1, (1,): 2, (2,): 3})
g = Signomial.from_dict({(-1,): 1})
h = Signomial.from_dict({(2, 3): 1,
(1, -3): -2})
x0 = -1.2345
x_test = 3.21
f_shift = f.shift_coordinates(x0)
self.assertAlmostEqual(f(x_test + x0), f_shift(x_test), places=4)
g_shift = g.shift_coordinates(x0)
self.assertAlmostEqual(g(x_test + x0), g_shift(x_test), places=4)
x0 = np.array([1.1, 2.2])
x_test = np.array([-0.5, 3])
h_shift = h.shift_coordinates(x0)
self.assertAlmostEqual(h(x_test + x0), h_shift(x_test), places=4)
self.assertRaises(ValueError, f.shift_coordinates, np.array([1, 1j]))
def poly_primal(f, poly_ell=0, sigrep_ell=0, X=None):
if poly_ell == 0:
sr, _ = f.sig_rep
prob = sage_sigs.sig_primal(sr, sigrep_ell, X=X)
if AUTO_CLEAR_INDICES: # pragma:no cover
cl.clear_variable_indices()
return prob
else:
poly_modulator = f.standard_multiplier()**poly_ell
gamma = cl.Variable(shape=(), name='gamma')
lagrangian = (f - gamma) * poly_modulator
if sigrep_ell > 0:
sr, cons = lagrangian.sig_rep
sig_modulator = Signomial(sr.alpha,
np.ones(shape=(sr.m, )))**sigrep_ell
sig_under_test = sr * sig_modulator
con_name = 'Lagrangian modulated sigrep sage'
con = sage_sigs.primal_sage_cone(sig_under_test, con_name, X=X)
constraints = [con] + cons
else:
con_name = 'Lagrangian sage poly'
constraints = primal_sage_poly_cone(lagrangian,
con_name,
log_AbK=X)
obj = gamma
prob = cl.Problem(cl.MAX, obj, constraints)
if AUTO_CLEAR_INDICES: # pragma:no cover
cl.clear_variable_indices()
return prob
def sig_constrained_primal(f, gts, eqs, p=0, q=1, ell=0, X=None):
"""
Construct the SAGE-(p, q, ell) primal problem for the signomial program
min{ f(x) : g(x) >= 0 for g in gts,
g(x) == 0 for g in eqs,
and x in X }
where X = :math:`R^{\\texttt{f.n}}` by default.
"""
lagrangian, ineq_lag_mults, _, gamma = make_sig_lagrangian(f, gts, eqs, p=p, q=q)
metadata = {'lagrangian': lagrangian, 'X': X}
if ell > 0:
alpha_E_1 = hierarchy_e_k([f, f.upcast_to_signomial(1)] + gts + eqs, k=1)
modulator = Signomial(alpha_E_1, np.ones(alpha_E_1.shape[0])) ** ell
lagrangian = lagrangian * modulator
else:
modulator = f.upcast_to_signomial(1)
metadata['modulator'] = modulator
# The Lagrangian (after possible multiplication, as above) must be a SAGE signomial.
con = primal_sage_cone(lagrangian, name='Lagrangian is SAGE', X=X)
constrs = [con]
# Lagrange multipliers (for inequality constraints) must be SAGE signomials.
expcovers = None
for i, (s_h, _) in enumerate(ineq_lag_mults):
con_name = 'SAGE multiplier for signomial inequality # ' + str(i)
con = primal_sage_cone(s_h, name=con_name, X=X, expcovers=expcovers)
expcovers = con.ech.expcovers # only * really * needed in first iteration, but keeps code flat.
constrs.append(con)
# Construct the coniclifts Problem.
prob = cl.Problem(cl.MAX, gamma, constrs)
prob.metadata = metadata
cl.clear_variable_indices()
return prob
def test_unconstrained_sage_3(self):
# Background
#
# This is Example 2.5 from the original SAGE paper by Chandrasekaran and Shah.
# The signomial s(x1,x2,x3) = (exp(x1) - exp(x2) - exp(x3))**2 is nonnegative
# over R^3, but it is not SAGE.
#
# Tests
#
# (1) Show that the standard SAGE hierarchy produces no finite bound on "s",
# for ell \in {0, 1}.
#
# Notes
#
# It is suspected that the standard SAGE hierarchy never produces a finite bound
# for this signomial.
#
s = Signomial.from_dict({(1, 0, 0): 1,
(0, 1, 0): -1,
(0, 0, 1): -1})
s = s ** 2
expected = -np.inf
pd0, _ = primal_dual_vals(s, 0)
assert pd0[0] == expected and pd0[1] == expected
pd1, _ = primal_dual_vals(s, 1)
assert pd1[0] == expected and pd1[1] == expected
def test_unconstrained_sage_2(self):
# Background
#
# This is Example 2 from a 2018 paper by Murray, Chandrasekaran, and Wierman
# (https://arxiv.org/pdf/1810.01614.pdf).
#
# Tests
#
# (1) Check that primal / dual objective are -\infty for ell == 0.
#
# (2) Check that primal / dual objectives are close to a reference value, for ell == 1.
#
# (3) Recover a globally optimal solution at ell == 1.
#
alpha = np.array([[0, 0],
[1, 0],
[0, 1],
[1, 1],
[0.5, 1],
[1, 0.5]])
c = np.array([0, 1, 1, 1.9, -2, -2])
s = Signomial(alpha, c)
expected = [-np.inf, -0.122211863]
pd0, _ = primal_dual_vals(s, 0)
assert pd0[0] == expected[0] and pd0[1] == expected[0]
pd1, dual = primal_dual_vals(s, 1)
assert abs(pd1[0] - expected[1]) < 1e-5 and abs(pd1[1] - expected[1]) < 1e-5
solns = sig_solrec(dual)
assert s(solns[0]) < 1e-6 + dual.value
def test_sage_multiplier_search(self):
# Background
#
# This example was constructed solely as a test case for sageopt.
#
# The problem is to find a bound on the nonnegative signomial
# s(x) = (exp(x) - exp(-x))**4, using the machinery of SAGE certificates.
#
# Tests
#
# (1) Show that there is no SAGE signomial "f" (over the same exponents as "s")
# such that f * s is SAGE.
#
# (2) Obtain a loose (but finite) bound on "s", via a SAGE relaxation with ell == 1.
#
# (3) Improve the finite bound from Test 2 by verifying nonnegativity of an
# appropriate translate of "s".
#
s = Signomial.from_dict({(1,): 1, (-1,): -1}) ** 4
prob0 = sage_multiplier_search(s, level=1)
res0 = prob0.solve(solver='ECOS', verbose=False)
val0 = res0[1]
assert val0 == -np.inf
prob1 = sig_relaxation(s, form='primal', ell=1)
res1 = prob1.solve(solver='ECOS', verbose=False)
s_bound = res1[1]
assert -np.inf < s_bound < 0
s_shifted = s - 0.5 * s_bound # shifted_s is nonnegative, and not-SAGE by construction.
prob2 = sage_multiplier_search(s_shifted, level=1)
res2 = prob2.solve(solver='ECOS', verbose=False)
val2 = res2[1]
assert val2 == 0.
def test_constrained_sage_2(self):
# Background
#
# This is a signomial formulation of a nonnegative polynomial optimization problem.
#
# The problem can be found on page 16 of the gloptipoly3 manual
# http://homepages.laas.fr/henrion/papers/gloptipoly3.pdf
# among other places. The optimal objective is -4.
#
# Tests - (p, q, ell) = (0, 1, 0)
#
# (1) Check for similar primal / dual objectives.
#
x = standard_sig_monomials(3)
f = -2 * x[0] + x[1] - x[2]
g1 = Signomial.from_dict({(0, 0, 0): 24,
(1, 0, 0): -20,
(0, 1, 0): 9,
(0, 0, 1): -13,
(2, 0, 0): 4,
(1, 1, 0): -4,
(1, 0, 1): 4,
(0, 2, 0): 2,
(0, 1, 1): -2,
(0, 0, 2): 2})
g2 = 4 - x[0] - x[1] - x[2]
g3 = 6 - 3*x[1] - x[2]
g4 = 2 - x[0]
g5 = 3 - x[2]
gts = [g1, g2, g3, g4, g5]
res01, _ = constrained_primal_dual_vals(f, gts, [], p=0, q=1, ell=0, X=None)
expect = -6
assert abs(res01[0] - expect) < 1e-4
assert abs(res01[1] - expect) < 1e-4
assert abs(res01[0] - res01[1]) < 1e-5
def _compute_sig_rep(self):
self._sig_rep = None
self._sig_rep_constrs = []
sigrep_c = np.zeros(shape=(self.m, ), dtype=object)
need_vars = []
for i, row in enumerate(self.alpha):
if np.any(row % 2 != 0):
if isinstance(self.c[i], __NUMERIC_TYPES__):
sigrep_c[i] = -abs(self.c[i])
elif self.c[i].is_constant():
sigrep_c[i] = -abs(self.c[i].value)
else:
need_vars.append(i)
else:
if isinstance(self.c[i], np.ndarray):
sigrep_c[i] = self.c[i][()]
else:
sigrep_c[i] = self.c[i]
if len(need_vars) > 0:
var_name = str(self) + ' variable sigrep coefficients'
c_hat = cl.Variable(shape=(len(need_vars), ), name=var_name)
sigrep_c[need_vars] = c_hat
self._sig_rep_constrs.append(c_hat <= self.c[need_vars])
self._sig_rep_constrs.append(c_hat <= -self.c[need_vars])
if sigrep_c.dtype == object:
sigrep_c = cl.Expression(sigrep_c)
self._sig_rep = Signomial(self.alpha, sigrep_c)
pass
def sig_solrec(prob, ineq_tol=1e-8, eq_tol=1e-6, skip_ls=False):
"""
Recover a list of candidate solutions from a dual SAGE relaxation. Solutions are
guaranteed to be feasible up to specified tolerances, but not necessarily optimal.
Parameters
----------
prob : coniclifts.Problem
A dual-form SAGE relaxation.
ineq_tol : float
The amount by which recovered solutions can violate inequality constraints.
eq_tol : float
The amount by which recovered solutions can violate equality constraints.
skip_ls : bool
Whether or not to skip constrained least-squares solution recovery.
Returns
-------
sols : list of ndarrays
A list of feasible solutions, sorted in increasing order of objective function value.
It is possible that this list is empty, in which case no feasible solutions were recovered.
"""
con = prob.constraints[0]
if not con.name == 'Lagrangian SAGE dual constraint': # pragma: no cover
raise RuntimeError('Unexpected first constraint in dual SAGE relaxation.')
metadata = prob.metadata
f = metadata['f']
# Recover any constraints present in "prob"
lag_gts, lag_eqs = [], []
if 'gts' in metadata:
# only happens in "constrained_sage_dual".
lag_gts = metadata['gts']
lag_eqs = metadata['eqs']
lagrangian = _make_dummy_lagrangian(f, lag_gts, lag_eqs)
if con.X is None:
X_gts, X_eqs = [], []
else:
X_gts, X_eqs = con.X.gts, con.X.eqs
gts = lag_gts + X_gts
eqs = lag_eqs + X_eqs
# Search for solutions which meet the feasibility criteria
v = con.v.value
v[v < 0] = 0
if np.any(np.isnan(v)):
return None
alpha = con.alpha
dummy_modulated_lagrangian = Signomial(alpha, np.ones(shape=(alpha.shape[0],)))
alpha_reduced = lagrangian.alpha
modulator = metadata['modulator']
M = moment_reduction_array(lagrangian, modulator, dummy_modulated_lagrangian)
if skip_ls:
sols0 = []
else:
sols0 = _least_squares_solution_recovery(alpha_reduced, con, v, M, gts, eqs, ineq_tol, eq_tol)
sols1 = _dual_age_cone_solution_recovery(con, v, M, gts, eqs, ineq_tol, eq_tol)
sols = sols0 + sols1
sols.sort(key=lambda mu: f(mu))
return sols
def __sub__(self, other):
if isinstance(other, Signomial) and not isinstance(other, Polynomial):
raise RuntimeError(
'Cannot subtract a signomial from a polynomial (or vice versa).'
)
temp = Signomial.__sub__(self, other)
temp = temp.as_polynomial()
return temp
def hierarchy_e_k(sigs, k):
alphas = [s.alpha for s in sigs]
alpha = np.vstack(alphas)
alpha = np.unique(alpha, axis=0)
c = np.ones(shape=(alpha.shape[0], ))
s = Signomial(alpha, c)
s = s**k
return s.alpha
def test_exponentiation(self):
x = standard_sig_monomials(2)
y0 = (x[0] - x[1])**2
y1 = x[0]**2 - 2 * x[0] * x[1] + x[1]**2
assert y0 == y1
z0 = x[0]**0.5
z1 = Signomial.from_dict({(0.5, 0): 1})
assert z0 == z1
def without_zeros(self):
"""
Return a Polynomial which is symbolically equivalent to ``self``,
but which doesn't track basis functions ``alpha[i,:]`` for which ``c[i] == 0``.
"""
p = Signomial.without_zeros(self)
p = p.as_polynomial()
return p
def test_signomial_evaluation(self):
s = Signomial.from_dict({(1, ): 1})
assert s(0) == 1 and abs(s(1) - np.exp(1)) < 1e-10
zero = np.array([0])
one = np.array([1])
assert s(zero) == 1 and abs(s(one) - np.exp(1)) < 1e-10
zero_one = np.array([[0, 1]])
assert np.allclose(s(zero_one), np.exp(zero_one), rtol=0, atol=1e-10)
def sig_constrained_dual(f, gts, eqs, p=0, q=1, ell=0, X=None, slacks=False):
"""
Construct the SAGE-(p, q, ell) dual problem for the signomial program
min{ f(x) : g(x) >= 0 for g in gts,
g(x) == 0 for g in eqs,
and x in X }
where X = :math:`R^{\\texttt{f.n}}` by default.
"""
lagrangian, ineq_lag_mults, eq_lag_mults, _ = make_sig_lagrangian(f, gts, eqs, p=p, q=q)
metadata = {'lagrangian': lagrangian, 'f': f, 'gts': gts, 'eqs': eqs, 'level': (p, q, ell), 'X': X}
if ell > 0:
alpha_E_1 = hierarchy_e_k([f, f.upcast_to_signomial(1)] + list(gts) + list(eqs), k=1)
modulator = Signomial(alpha_E_1, np.ones(alpha_E_1.shape[0])) ** ell
lagrangian = lagrangian * modulator
f = f * modulator
else:
modulator = f.upcast_to_signomial(1)
metadata['modulator'] = modulator
# In primal form, the Lagrangian is constrained to be a SAGE signomial.
# Introduce a dual variable "v" for this constraint.
v = cl.Variable(shape=(lagrangian.m, 1), name='v')
con = relative_dual_sage_cone(lagrangian, v, name='Lagrangian SAGE dual constraint', X=X)
constraints = [con]
expcovers = None
for i, (s_h, h) in enumerate(ineq_lag_mults):
# These generalized Lagrange multipliers "s_h" are SAGE signomials.
# For each such multiplier, introduce an appropriate dual variable "v_h", along
# with constraints over that dual variable.
h_m = h * modulator
c_h = sym_corr.moment_reduction_array(s_h, h_m, lagrangian)
if slacks:
v_h = cl.Variable(name='v_' + str(h), shape=(s_h.m, 1))
constraints.append(c_h @ v == v_h)
else:
v_h = c_h @ v
con_name = 'SAGE dual for signomial inequality # ' + str(i)
con = relative_dual_sage_cone(s_h, v_h, name=con_name, X=X, expcovers=expcovers)
expcovers = con.ech.expcovers # only * really * needed in first iteration, but keeps code flat.
constraints.append(con)
for s_h, h in eq_lag_mults:
# These generalized Lagrange multipliers "s_h" are arbitrary signomials.
# They dualize to homogeneous equality constraints.
h = h * modulator
c_h = sym_corr.moment_reduction_array(s_h, h, lagrangian)
constraints.append(c_h @ v == 0)
# Equality constraint (for the Lagrangian to be bounded).
a = sym_corr.relative_coeff_vector(modulator, lagrangian.alpha)
constraints.append(a.T @ v == 1)
# Define the dual objective function.
obj_vec = sym_corr.relative_coeff_vector(f, lagrangian.alpha)
obj = obj_vec.T @ v
# Return the coniclifts Problem.
prob = cl.Problem(cl.MIN, obj, constraints)
prob.metadata = metadata
cl.clear_variable_indices()
return prob
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
def as_signomial(self):
"""
Returns
-------
f : Signomial
For every elementwise positive vector ``x``, we have ``self(x) == f(np.log(x))``.
"""
f = Signomial(self.alpha, self.c)
return f
def gpkit_hmap_to_sageopt_sig(curhmap, vkmap):
n_vks = len(vkmap)
temp_sig_dict = dict()
for expinfo, coeff in curhmap.items():
tup = n_vks * [0]
for vk, expval in expinfo.items():
tup[vkmap[vk]] = expval
temp_sig_dict[tuple(tup)] = coeff
s = Signomial.from_dict(temp_sig_dict)
return s
def _constrained_sage_1():
# Background
#
# This is Example 3.3 from Chandraskearan and Shah's original paper on SAGE relaxations.
# The problem is to minimize a nonconvex signomial, over a convex set defined by a single
# posynomial inequality.
#
s0 = Signomial.from_dict({(10.2, 0, 0): 10, (0, 9.8, 0): 10, (0, 0, 8.2): 10})
s1 = Signomial.from_dict({(1.5089, 1.0981, 1.3419): -14.6794})
s2 = Signomial.from_dict({(1.0857, 1.9069, 1.6192): -7.8601})
s3 = Signomial.from_dict({(1.0459, 0.0492, 1.6245): 8.7838})
f = s0 + s1 + s2 + s3
g = Signomial.from_dict({(10.2, 0, 0): -8,
(0, 9.8, 0): -8,
(0, 0, 8.2): -8,
(1.0857, 1.9069, 1.6192): -6.4,
(0, 0, 0): 1})
gs = [g]
return f, gs
def valid_monomial_equations(eqs):
conv_eqs = []
for g in eqs:
# g defines a constraint g(x) == 0.
if np.count_nonzero(g.c) > 2:
# cannot convexify
continue
pos_loc = np.where(g.c > 0)[0]
if pos_loc.size == 1:
pos_loc = pos_loc[0]
inverse_term = Signomial.from_dict(
{tuple(-g.alpha[pos_loc, :]): 1})
conv_eqs.append(g * inverse_term)
return conv_eqs
def test_broadcasting(self):
# any signomial will do.
alpha_c = {(0, ): 1, (1, ): -1, (2, ): -2}
s = Signomial.from_dict(alpha_c)
other = np.array([1, 2])
t1 = s + other
self.assertIsInstance(t1, np.ndarray)
t2 = other + s
self.assertIsInstance(t2, np.ndarray)
delta = t1 - t2
d1 = delta[0].without_zeros()
d2 = delta[1].without_zeros()
self.assertEqual(d1.m, 1)
self.assertEqual(d2.m, 1)
