def convexify_constr(constr):
"""
:param constr: a constraint of a problem
:return:
for a dcp constraint, return itself;
for a non-dcp constraint, return a convexified constraint and domain constraints;
return None if non-sub/super-diff
"""
if not constr.is_dcp():
dom = []
# left hand concave
if constr.args[0].curvature == 'CONCAVE':
left = linearize(constr.args[0])
if left is None:
return None
else:
for con in constr.args[0].domain:
dom.append(con)
else:
left = constr.args[0]
# right hand convex
if constr.args[1].curvature == 'CONVEX':
right = linearize(constr.args[1])
if right is None:
return None
else:
for con in constr.args[1].domain:
dom.append(con)
else:
right = constr.args[1]
# quick hack to get fairness-classification methods working, but also means any input needs to be real-valued
# return cvx.real(left - right) <= 0, dom
return left - right <= 0, dom
else:
return constr
def convexify_constr(constr):
"""
:param constr: a constraint of a problem
:return:
for a dcp constraint, return itself;
for a non-dcp constraint, return a convexified constraint and domain constraints;
return None if non-sub/super-diff
"""
if not constr.is_dcp():
dom = []
# left hand concave
if constr.args[0].curvature == 'CONCAVE':
left = linearize(constr.args[0])
if left is None:
return None
else:
for con in constr.args[0].domain:
dom.append(con)
else:
left = constr.args[0]
# right hand convex
if constr.args[1].curvature == 'CONVEX':
right = linearize(constr.args[1])
if right is None:
return None
else:
for con in constr.args[1].domain:
dom.append(con)
else:
right = constr.args[1]
return left - right <= 0, dom
else:
return constr
def convexify_constr(constr):
"""
:param constr: a constraint of a problem
:return:
for a dcp constraint, return itself;
for a non-dcp constraint, return a convexified constraint and domain constraints;
return None if non-sub/super-diff
"""
if not constr.is_dcp():
dom = []
# left hand concave
if constr.args[0].curvature == 'CONCAVE':
left = linearize(constr.args[0])
if left is None:
return None
else:
for con in constr.args[0].domain:
dom.append(con)
else:
left = constr.args[0]
# right hand convex
if constr.args[1].curvature == 'CONVEX':
right = linearize(constr.args[1])
if right is None:
return None
else:
for con in constr.args[1].domain:
dom.append(con)
else:
right = constr.args[1]
return left - right <= 0, dom
else:
return constr
def test_linearize(self):
"""Test the linearize function.
"""
z = Variable(1,5)
expr = square(z)
z.value = np.reshape(np.array([1,2,3,4,5]), (1,5))
lin = linearize(expr)
self.assertEqual(lin.size, (1,5))
self.assertItemsAlmostEqual(lin.value, [1,4,9,16,25])
def test_linearize(self):
"""Test the linearize function.
"""
z = Variable(1, 5)
expr = square(z)
z.value = np.reshape(np.array([1, 2, 3, 4, 5]), (1, 5))
lin = linearize(expr)
self.assertEqual(lin.size, (1, 5))
self.assertItemsAlmostEqual(lin.value, [1, 4, 9, 16, 25])
def convexify_obj(obj, vars=None, grads=None):
"""
:param obj: objective of a problem
:return: convexified onjective or None
"""
# not dcp
if obj.is_dcp() == False:
lin = linearize(obj.expr, vars=vars, grads=grads)
# non-sub/super-diff
if lin is None:
return None
else:
if obj.NAME == 'minimize':
result = cvx.Minimize(lin)
else:
result = cvx.Maximize(lin)
else:
result = obj
return result
def convexify_obj(obj):
"""
:param obj: objective of a problem
:return: convexified onjective or None
"""
# not dcp
if obj.is_dcp() == False:
lin = linearize(obj.expr)
# non-sub/super-diff
if lin is None:
return None
else:
if obj.NAME == 'minimize':
result = cvx.Minimize(lin)
else:
result = cvx.Maximize(lin)
else:
result = obj
return result
