def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
w = lu.create_var(size)
v = lu.create_var(size)
two = lu.create_const(2, (1, 1))
# w**2 + 2*v
obj, constraints = square.graph_implementation([w], size)
obj = lu.sum_expr([obj, lu.mul_expr(two, v, size)])
# x <= w + v
constraints.append(lu.create_leq(x, lu.sum_expr([w, v])))
# v >= 0
constraints.append(lu.create_geq(v))
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
# min sum_entries(t) + kq
# s.t. x <= t + q
# 0 <= t
x = arg_objs[0]
k = lu.create_const(data[0], (1, 1))
q = lu.create_var((1, 1))
t = lu.create_var(x.size)
sum_t, constr = sum_entries.graph_implementation([t], (1, 1))
obj = lu.sum_expr([sum_t, lu.mul_expr(k, q, (1, 1))])
prom_q = lu.promote(q, x.size)
constr.append(lu.create_leq(x, lu.sum_expr([t, prom_q])))
constr.append(lu.create_geq(t))
return (obj, constr)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
w = lu.create_var(size)
v = lu.create_var(size)
two = lu.create_const(2, (1, 1))
# w**2 + 2*v
obj, constraints = square.graph_implementation([w], size)
obj = lu.sum_expr([obj, lu.mul_expr(two, v, size)])
# x <= w + v
constraints.append(lu.create_leq(x, lu.sum_expr([w, v])))
# v >= 0
constraints.append(lu.create_geq(v))
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
# min sum_entries(t) + kq
# s.t. x <= t + q
# 0 <= t
x = arg_objs[0]
k = lu.create_const(data[0], (1, 1))
q = lu.create_var((1, 1))
t = lu.create_var(x.size)
sum_t, constr = sum_entries.graph_implementation([t], (1, 1))
obj = lu.sum_expr([sum_t, lu.mul_expr(k, q, (1, 1))])
prom_q = lu.promote(q, x.size)
constr.append( lu.create_leq(x, lu.sum_expr([t, prom_q])) )
constr.append( lu.create_geq(t) )
return (obj, constr)
def constraints(self):
obj, constraints = super(BoolVar, self).canonicalize()
one = lu.create_const(1, (1, 1))
constraints += [lu.create_geq(obj),
lu.create_leq(obj, one)]
for i in range(self.size[0]):
row_sum = lu.sum_expr([self[i, j] for j in range(self.size[0])])
col_sum = lu.sum_expr([self[j, i] for j in range(self.size[0])])
constraints += [lu.create_eq(row_sum, one),
lu.create_eq(col_sum, one)]
return constraints
def graph_implementation(arg_objs, size, data=None):
# min 1-sqrt(2z-z^2)
# s.t. x>=0, z<=1, z = x+s, s<=0
x = arg_objs[0]
z = lu.create_var(size)
s = lu.create_var(size)
zeros = lu.create_const(np.mat(np.zeros(size)),size)
ones = lu.create_const(np.mat(np.ones(size)),size)
z2, constr_square = power.graph_implementation([z],size, (2, (Fraction(1,2), Fraction(1,2))))
two_z = lu.sum_expr([z,z])
sub = lu.sub_expr(two_z, z2)
sq, constr_sqrt = power.graph_implementation([sub],size, (Fraction(1,2), (Fraction(1,2), Fraction(1,2))))
obj = lu.sub_expr(ones, sq)
constr = [lu.create_eq(z, lu.sum_expr([x,s]))]+[lu.create_leq(zeros,x)]+[lu.create_leq(z, ones)]+[lu.create_leq(s,zeros)]+constr_square+constr_sqrt
return (obj, constr)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1] # Known to be a scalar.
v = lu.create_var((1, 1))
two = lu.create_const(2, (1, 1))
constraints = [SOC(lu.sum_expr([y, v]),
[lu.sub_expr(y, v),
lu.mul_expr(two, x, x.size)]),
lu.create_geq(y)]
return (v, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
# Promote scalars.
for idx, arg in enumerate(arg_objs):
if arg.size != size:
arg_objs[idx] = lu.promote(arg, size)
x = arg_objs[0]
y = arg_objs[1]
v = lu.create_var(x.size)
two = lu.create_const(2, (1, 1))
# SOC(x + y, [y - x, 2*v])
constraints = [
SOC_Elemwise(lu.sum_expr([x, y]),
[lu.sub_expr(y, x),
lu.mul_expr(two, v, v.size)])
]
# 0 <= x, 0 <= y
constraints += [lu.create_geq(x), lu.create_geq(y)]
return (v, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
t = lu.create_var(size)
# log(1 + exp(x)) <= t <=> exp(-t) + exp(x - t) <= 1
obj0, constr0 = exp.graph_implementation([lu.neg_expr(t)], size)
obj1, constr1 = exp.graph_implementation([lu.sub_expr(x, t)], size)
lhs = lu.sum_expr([obj0, obj1])
ones = lu.create_const(np.mat(np.ones(size)), size)
constr = constr0 + constr1 + [lu.create_leq(lhs, ones)]
return (t, constr)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
t = lu.create_var((1, 1))
promoted_t = lu.promote(t, x.size)
constraints = [
lu.create_geq(lu.sum_expr([x, promoted_t])),
lu.create_leq(x, promoted_t)
]
return (t, constraints)
def qol_elemwise(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1]
t = lu.create_var(x.size)
two = lu.create_const(2, (1, 1))
constraints = [SOC_Elemwise(lu.sum_expr([y, t]),
[lu.sub_expr(y, t),
lu.mul_expr(two, x, x.size)]),
lu.create_geq(y)]
return (t, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
t = lu.create_var(size)
# log(1 + exp(x)) <= t <=> exp(-t) + exp(x - t) <= 1
obj0, constr0 = exp.graph_implementation([lu.neg_expr(t)], size)
obj1, constr1 = exp.graph_implementation([lu.sub_expr(x, t)], size)
lhs = lu.sum_expr([obj0, obj1])
ones = lu.create_const(np.mat(np.ones(size)), size)
constr = constr0 + constr1 + [lu.create_leq(lhs, ones)]
return (t, constr)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
# Promote scalars.
for idx, arg in enumerate(arg_objs):
if arg.size != size:
arg_objs[idx] = lu.promote(arg, size)
x = arg_objs[0]
y = arg_objs[1]
v = lu.create_var(x.size)
two = lu.create_const(2, (1, 1))
# SOC(x + y, [y - x, 2*v])
constraints = [
SOC_Elemwise(lu.sum_expr([x, y]),
[lu.sub_expr(y, x),
lu.mul_expr(two, v, v.size)])
]
# 0 <= x, 0 <= y
constraints += [lu.create_geq(x), lu.create_geq(y)]
return (v, constraints)
def qol_elemwise(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1]
t = lu.create_var(x.size)
two = lu.create_const(2, (1, 1))
constraints = [
SOC_Elemwise(
lu.sum_expr([y, t]),
[lu.sub_expr(y, t), lu.mul_expr(two, x, x.size)]),
lu.create_geq(y)
]
return (t, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1]
t = lu.create_var((1, 1))
constraints = [ExpCone(t, x, y),
lu.create_geq(y)] # 0 <= y
# -t - x + y
obj = lu.sub_expr(y, lu.sum_expr([x, t]))
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1] # Known to be a scalar.
v = lu.create_var((1, 1))
two = lu.create_const(2, (1, 1))
constraints = [
SOC(lu.sum_expr([y, v]),
[lu.sub_expr(y, v),
lu.mul_expr(two, x, x.size)]),
lu.create_geq(y)
]
return (v, constraints)
def constr_func(aff_obj):
theta = [lu.create_var((1, 1)) for i in range(len(values))]
convex_objs = []
for val, theta_var in zip(values, theta):
val_aff = val.canonical_form[0]
convex_objs.append(
lu.mul_expr(val_aff, theta_var, val_aff.size))
convex_combo = lu.sum_expr(convex_objs)
one = lu.create_const(1, (1, 1))
constraints = [
lu.create_eq(aff_obj, convex_combo),
lu.create_eq(lu.sum_expr(theta), one)
]
for theta_var in theta:
constraints.append(lu.create_geq(theta_var))
return constraints
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
# TODO use log for n != 2.
v = lu.create_var((1, 1))
x = arg_objs[0]
y = arg_objs[1]
two = lu.create_const(2, (1, 1))
# SOC(x + y, [y - x, 2*v])
constraints = [
SOC(lu.sum_expr([x, y]),
[lu.sub_expr(y, x),
lu.mul_expr(two, v, (1, 1))])
]
# 0 <= x, 0 <= y
constraints += [lu.create_geq(x), lu.create_geq(y)]
return (v, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
y = arg_objs[1]
t = lu.create_var((1, 1))
constraints = [ExpCone(t, x, y),
lu.create_geq(y)] # 0 <= y
# -t - x + y
obj = lu.sub_expr(y, lu.sum_expr([x, t]))
return (obj, constraints)
def gm(t, x, y):
length = t.size[0] * t.size[1]
return SOC_Axis(
lu.reshape(lu.sum_expr([x, y]), (length, 1)),
lu.vstack([
lu.reshape(lu.sub_expr(x, y), (1, length)),
lu.reshape(lu.mul_expr(two, t, t.size), (1, length))
], (2, length)), 0)
def gm(t, x, y):
length = t.size[0]*t.size[1]
return SOC_Axis(lu.reshape(lu.sum_expr([x, y]), (length, 1)),
lu.vstack([
lu.reshape(lu.sub_expr(x, y), (1, length)),
lu.reshape(lu.mul_expr(two, t, t.size), (1, length))
], (2, length)),
0)
def constr_func(aff_obj):
theta = [lu.create_var((1, 1)) for i in xrange(len(values))]
convex_objs = []
for val, theta_var in zip(values, theta):
val_aff = val.canonical_form[0]
convex_objs.append(
lu.mul_expr(val_aff,
theta_var,
val_aff.size)
)
convex_combo = lu.sum_expr(convex_objs)
one = lu.create_const(1, (1, 1))
constraints = [lu.create_eq(aff_obj, convex_combo),
lu.create_eq(lu.sum_expr(theta), one)]
for theta_var in theta:
constraints.append(lu.create_geq(theta_var))
return constraints
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
t = lu.create_var(size)
# log(1 + exp(x)) <= t <=> exp(-t) + exp(x - t) <= 1
'''
obj0, constr0 = exp.graph_implementation([lu.neg_expr(t)], size)
obj1, constr1 = exp.graph_implementation([lu.sub_expr(x, t)], size)
lhs = lu.sum_expr([obj0, obj1])
ones = lu.create_const(np.mat(np.ones(size)), size)
constr = constr0 + constr1 + [lu.create_leq(lhs, ones)]
'''
s = data[0]
if isinstance(s, Parameter):
s = lu.create_param(s, (1, 1))
else: # M is constant.
s = lu.create_const(s, (1, 1))
#Wrong sign?
obj0, constr0 = exp.graph_implementation([lu.neg_expr(t)], size)
obj1, constr1 = exp.graph_implementation([lu.sub_expr(s, lu.sum_expr([t, x]))], size)
obj2, constr2 = exp.graph_implementation([lu.sub_expr(lu.neg_expr(s), lu.sum_expr([t, x]))], size)
obj3, constr3 = exp.graph_implementation([lu.sub_expr(lu.neg_expr(t), lu.mul_expr(2, x, size))], size)
lhs = lu.sum_expr([obj0, obj1, obj2, obj3])
ones = lu.create_const(np.mat(np.ones(size)), size)
constr = constr0 + constr1 + constr2 + constr3 + [lu.create_leq(lhs, ones)]
return (t, constr)
def test_add_expr(self):
"""Test adding lin expr.
"""
shape = (5, 4)
x = create_var(shape)
y = create_var(shape)
# Expanding dict.
add_expr = sum_expr([x, y])
self.assertEqual(add_expr.shape, shape)
assert len(add_expr.args) == 2
def graph_implementation(arg_objs, shape, data=None):
"""Reduces the atom to an affine expression and list of constraints.
minimize n^2 + 2M|s|
subject to s + n = x
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
M = data[0]
x = arg_objs[0]
n = lu.create_var(shape)
s = lu.create_var(shape)
two = lu.create_const(2, (1, 1))
if isinstance(M, Parameter):
M = lu.create_param(M, (1, 1))
else: # M is constant.
M = lu.create_const(M.value, (1, 1))
# n**2 + 2*M*|s|
n2, constr_sq = power.graph_implementation(
[n],
shape, (2, (Fraction(1, 2), Fraction(1, 2)))
)
abs_s, constr_abs = abs.graph_implementation([s], shape)
M_abs_s = lu.mul_expr(M, abs_s)
obj = lu.sum_expr([n2, lu.mul_expr(two, M_abs_s)])
# x == s + n
constraints = constr_sq + constr_abs
constraints.append(lu.create_eq(x, lu.sum_expr([n, s])))
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
# min 1-sqrt(2z-z^2)
# s.t. x>=0, z<=1, z = x+s, s<=0
x = arg_objs[0]
z = lu.create_var(size)
s = lu.create_var(size)
zeros = lu.create_const(np.mat(np.zeros(size)), size)
ones = lu.create_const(np.mat(np.ones(size)), size)
z2, constr_square = power.graph_implementation(
[z], size, (2, (Fraction(1, 2), Fraction(1, 2))))
two_z = lu.sum_expr([z, z])
sub = lu.sub_expr(two_z, z2)
sq, constr_sqrt = power.graph_implementation(
[sub], size, (Fraction(1, 2), (Fraction(1, 2), Fraction(1, 2))))
obj = lu.sub_expr(ones, sq)
constr = [lu.create_eq(z, lu.sum_expr([x, s]))] + [
lu.create_leq(zeros, x)
] + [lu.create_leq(z, ones)] + [lu.create_leq(s, zeros)
] + constr_square + constr_sqrt
return (obj, constr)
def graph_implementation(arg_objs,size,data=None):
x = arg_objs[0]
beta,x0 = data[0],data[1]
beta_val,x0_val = beta.value,x0.value
if isinstance(beta,Parameter):
beta = lu.create_param(beta,(1,1))
else:
beta = lu.create_const(beta.value,(1,1))
if isinstance(x0,Parameter):
x0 = lu.create_param(x0,(1,1))
else:
x0 = lu.create_const(x0.value,(1,1))
xi,psi = lu.create_var(size),lu.create_var(size)
one = lu.create_const(1,(1,1))
one_over_beta = lu.create_const(1/beta_val,(1,1))
k = np.exp(-beta_val*x0_val)
k = lu.create_const(k,(1,1))
# 1/beta * (1 - exp(-beta*(xi+x0)))
xi_plus_x0 = lu.sum_expr([xi,x0])
minus_beta_times_xi_plus_x0 = lu.neg_expr(lu.mul_expr(beta,xi_plus_x0,size))
exp_xi,constr_exp = exp.graph_implementation([minus_beta_times_xi_plus_x0],size)
minus_exp_minus_etc = lu.neg_expr(exp_xi)
left_branch = lu.mul_expr(one_over_beta, lu.sum_expr([one,minus_exp_minus_etc]),size)
# psi*exp(-beta*r0)
right_branch = lu.mul_expr(k,psi,size)
obj = lu.sum_expr([left_branch,right_branch])
#x-x0 == xi + psi, xi >= 0, psi <= 0
zero = lu.create_const(0,size)
constraints = constr_exp
prom_x0 = lu.promote(x0, size)
constraints.append(lu.create_eq(x,lu.sum_expr([prom_x0,xi,psi])))
constraints.append(lu.create_geq(xi,zero))
constraints.append(lu.create_leq(psi,zero))
return (obj, constraints)
def test_get_vars(self):
"""Test getting vars from an expression.
"""
shape = (5, 4)
x = create_var(shape)
y = create_var(shape)
A = create_const(np.ones(shape), shape)
# Expanding dict.
add_expr = sum_expr([x, y, A])
vars_ = get_expr_vars(add_expr)
ref = [(x.data, shape), (y.data, shape)]
self.assertCountEqual(vars_, ref)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
minimize n^2 + 2M|s|
subject to s + n = x
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
M = data
x = arg_objs[0]
n = lu.create_var(size)
s = lu.create_var(size)
two = lu.create_const(2, (1, 1))
if isinstance(M, Parameter):
M = lu.create_param(M, (1, 1))
else: # M is constant.
M = lu.create_const(M.value, (1, 1))
# n**2 + 2*M*|s|
n2, constr_sq = square.graph_implementation([n], size)
abs_s, constr_abs = abs.graph_implementation([s], size)
M_abs_s = lu.mul_expr(M, abs_s, size)
obj = lu.sum_expr([n2, lu.mul_expr(two, M_abs_s, size)])
# x == s + n
constraints = constr_sq + constr_abs
constraints.append(lu.create_eq(x, lu.sum_expr([n, s])))
return (obj, constraints)
def test_leq_constr(self):
"""Test creating a less than or equal constraint.
"""
shape = (5, 5)
x = create_var(shape)
y = create_var(shape)
lh_expr = sum_expr([x, y])
value = np.ones(shape)
rh_expr = create_const(value, shape)
constr = create_leq(lh_expr, rh_expr)
self.assertEqual(constr.shape, shape)
vars_ = get_expr_vars(constr.expr)
ref = [(x.data, shape), (y.data, shape)]
self.assertCountEqual(vars_, ref)
def canonicalize(self):
"""Returns the graph implementation of the object.
Marks the top level constraint as the dual_holder,
so the dual value will be saved to the EqConstraint.
Returns:
A tuple of (affine expression, [constraints]).
"""
obj, constraints = self._expr.canonical_form
half = lu.create_const(0.5, (1, 1))
symm = lu.mul_expr(half, lu.sum_expr([obj, lu.transpose(obj)]),
obj.size)
dual_holder = SDP(symm, enforce_sym=False, constr_id=self.id)
return (None, constraints + [dual_holder])
def canonicalize(self):
"""Returns the graph implementation of the object.
Marks the top level constraint as the dual_holder,
so the dual value will be saved to the EqConstraint.
Returns:
A tuple of (affine expression, [constraints]).
"""
obj, constraints = self._expr.canonical_form
half = lu.create_const(0.5, (1,1))
symm = lu.mul_expr(half, lu.sum_expr([obj, lu.transpose(obj)]),
obj.size)
dual_holder = SDP(symm, enforce_sym=False, constr_id=self.id)
return (None, constraints + [dual_holder])
def test_eq_constr(self):
"""Test creating an equality constraint.
"""
shape = (5, 5)
x = create_var(shape)
y = create_var(shape)
lh_expr = sum_expr([x, y])
value = np.ones(shape)
rh_expr = create_const(value, shape)
constr = create_eq(lh_expr, rh_expr)
self.assertEqual(constr.shape, shape)
vars_ = get_expr_vars(constr.expr)
ref = [(x.data, shape), (y.data, shape)]
if PY2:
self.assertItemsEqual(vars_, ref)
else:
self.assertCountEqual(vars_, ref)
def format_axis(t, X, axis):
"""Formats all the row/column cones for the solver.
Parameters
----------
t: The scalar part of the second-order constraint.
X: A matrix whose rows/columns are each a cone.
axis: Slice by column 0 or row 1.
Returns
-------
list
A list of LinLeqConstr that represent all the elementwise cones.
"""
# Reduce to norms of columns.
if axis == 1:
X = lu.transpose(X)
# Create matrices Tmat, Xmat such that Tmat*t + Xmat*X
# gives the format for the elementwise cone constraints.
cone_size = 1 + X.shape[0]
terms = []
# Make t_mat
mat_shape = (cone_size, 1)
t_mat = sp.coo_matrix(([1.0], ([0], [0])), mat_shape).tocsc()
t_mat = lu.create_const(t_mat, mat_shape, sparse=True)
t_vec = t
if not t.shape:
# t is scalar
t_vec = lu.reshape(t, (1, 1))
else:
# t is 1D
t_vec = lu.reshape(t, (1, t.shape[0]))
mul_shape = (cone_size, t_vec.shape[1])
terms += [lu.mul_expr(t_mat, t_vec, mul_shape)]
# Make X_mat
if len(X.shape) == 1:
X = lu.reshape(X, (X.shape[0], 1))
mat_shape = (cone_size, X.shape[0])
val_arr = (cone_size - 1) * [1.0]
row_arr = list(range(1, cone_size))
col_arr = list(range(cone_size - 1))
X_mat = sp.coo_matrix((val_arr, (row_arr, col_arr)), mat_shape).tocsc()
X_mat = lu.create_const(X_mat, mat_shape, sparse=True)
mul_shape = (cone_size, X.shape[1])
terms += [lu.mul_expr(X_mat, X, mul_shape)]
return [lu.create_geq(lu.sum_expr(terms))]
def graph_implementation(arg_objs, size, data=None):
"""Sum the linear expressions.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
return (lu.sum_expr(arg_objs), [])
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
ones = lu.create_const(np.mat(np.ones(x.size)), x.size)
xp1 = lu.sum_expr([x, ones])
return log.graph_implementation([xp1], size, data)
def graph_implementation(arg_objs, shape, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
ones = lu.create_const(np.mat(np.ones(x.shape)), x.shape)
xp1 = lu.sum_expr([x, ones])
return log.graph_implementation([xp1], shape, data)
def graph_implementation(arg_objs, size, data=None):
"""Sum the linear expressions.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
for i, arg in enumerate(arg_objs):
if arg.size != size:
arg_objs[i] = lu.promote(arg, size)
return (lu.sum_expr(arg_objs), [])
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
x = arg_objs[0]
t = lu.create_var((1, 1))
promoted_t = lu.promote(t, x.size)
constraints = [lu.create_geq(lu.sum_expr([x, promoted_t])), lu.create_leq(x, promoted_t)]
return (t, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
A = arg_objs[0]
rows, cols = A.size
# Create the equivalent problem:
# minimize (trace(U) + trace(V))/2
# subject to:
# [U A; A.T V] is positive semidefinite
X = lu.create_var((rows + cols, rows + cols))
# Expand A.T.
obj, constraints = transpose.graph_implementation([A], (cols, rows))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:rows,rows:rows+cols] == A
index.block_eq(X, A, constraints, 0, rows, rows, rows + cols)
# X[rows:rows+cols,0:rows] == A.T
index.block_eq(X, obj, constraints, rows, rows + cols, 0, rows)
diag = [
index.get_index(X, constraints, i, i) for i in range(rows + cols)
]
half = lu.create_const(0.5, (1, 1))
trace = lu.mul_expr(half, lu.sum_expr(diag), (1, 1))
# Add SDP constraint.
return (trace, [SDP(X)] + constraints)
def format_axis(t, X, axis):
"""Formats all the row/column cones for the solver.
Parameters
----------
t: The scalar part of the second-order constraint.
X: A matrix whose rows/columns are each a cone.
axis: Slice by column 0 or row 1.
Returns
-------
list
A list of LinLeqConstr that represent all the elementwise cones.
"""
# Reduce to norms of columns.
if axis == 1:
X = lu.transpose(X)
# Create matrices Tmat, Xmat such that Tmat*t + Xmat*X
# gives the format for the elementwise cone constraints.
num_cones = t.size[0]
cone_size = 1 + X.size[0]
terms = []
# Make t_mat
mat_size = (cone_size, 1)
prod_size = (cone_size, t.size[0])
t_mat = sp.coo_matrix(([1.0], ([0], [0])), mat_size).tocsc()
t_mat = lu.create_const(t_mat, mat_size, sparse=True)
terms += [lu.mul_expr(t_mat, lu.transpose(t), prod_size)]
# Make X_mat
mat_size = (cone_size, X.size[0])
prod_size = (cone_size, X.size[1])
val_arr = (cone_size - 1)*[1.0]
row_arr = range(1, cone_size)
col_arr = range(cone_size-1)
X_mat = sp.coo_matrix((val_arr, (row_arr, col_arr)), mat_size).tocsc()
X_mat = lu.create_const(X_mat, mat_size, sparse=True)
terms += [lu.mul_expr(X_mat, X, prod_size)]
return [lu.create_geq(lu.sum_expr(terms))]
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
A = arg_objs[0]
rows, cols = A.size
# Create the equivalent problem:
# minimize (trace(U) + trace(V))/2
# subject to:
# [U A; A.T V] is positive semidefinite
X = lu.create_var((rows+cols, rows+cols))
# Expand A.T.
obj, constraints = transpose.graph_implementation([A], (cols, rows))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:rows,rows:rows+cols] == A
index.block_eq(X, A, constraints,
0, rows, rows, rows+cols)
# X[rows:rows+cols,0:rows] == A.T
index.block_eq(X, obj, constraints,
rows, rows+cols, 0, rows)
diag = [index.get_index(X, constraints, i, i) for i in range(rows+cols)]
half = lu.create_const(0.5, (1, 1))
trace = lu.mul_expr(half, lu.sum_expr(diag), (1, 1))
# Add SDP constraint.
return (trace, [SDP(X)] + constraints)
def format_elemwise(vars_):
"""Formats all the elementwise cones for the solver.
Parameters
----------
vars_ : list
A list of the LinOp expressions in the elementwise cones.
Returns
-------
list
A list of LinLeqConstr that represent all the elementwise cones.
"""
# Create matrices Ai such that 0 <= A0*x0 + ... + An*xn
# gives the format for the elementwise cone constraints.
spacing = len(vars_)
# Matrix spaces out columns of the LinOp expressions.
mat_shape = (spacing * vars_[0].shape[0], vars_[0].shape[0])
terms = []
for i, var in enumerate(vars_):
mat = get_spacing_matrix(mat_shape, spacing, i)
terms.append(lu.mul_expr(mat, var))
return [lu.create_geq(lu.sum_expr(terms))]
def graph_implementation(self,
arg_objs,
shape: Tuple[int, ...],
data=None) -> Tuple[lo.LinOp, List[Constraint]]:
"""Sum the linear expressions.
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
for i, arg in enumerate(arg_objs):
if arg.shape != shape and lu.is_scalar(arg):
arg_objs[i] = lu.promote(arg, shape)
return (lu.sum_expr(arg_objs), [])
def format_elemwise(vars_):
"""Formats all the elementwise cones for the solver.
Parameters
----------
vars_ : list
A list of the LinOp expressions in the elementwise cones.
Returns
-------
list
A list of LinLeqConstr that represent all the elementwise cones.
"""
# Create matrices Ai such that 0 <= A0*x0 + ... + An*xn
# gives the format for the elementwise cone constraints.
spacing = len(vars_)
prod_size = (spacing*vars_[0].size[0], vars_[0].size[1])
# Matrix spaces out columns of the LinOp expressions.
mat_size = (spacing*vars_[0].size[0], vars_[0].size[0])
terms = []
for i, var in enumerate(vars_):
mat = get_spacing_matrix(mat_size, spacing, i)
terms.append(lu.mul_expr(mat, var, prod_size))
return [lu.create_geq(lu.sum_expr(terms))]
def gm(t, x, y):
return SOC_Elemwise(lu.sum_expr([x, y]),
[lu.sub_expr(x, y),
lu.mul_expr(two, t, t.size)])
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Creates the equivalent problem::
maximize sum(log(D[i, i]))
subject to: D diagonal
diag(D) = diag(Z)
Z is upper triangular.
[D Z; Z.T A] is positive semidefinite
The problem computes the LDL factorization:
.. math::
A = (Z^TD^{-1})D(D^{-1}Z)
This follows from the inequality:
.. math::
\det(A) >= \det(D) + \det([D, Z; Z^T, A])/\det(D)
>= \det(D)
because (Z^TD^{-1})D(D^{-1}Z) is a feasible D, Z that achieves
det(A) = det(D) and the objective maximizes det(D).
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
A = arg_objs[0] # n by n matrix.
n, _ = A.size
X = lu.create_var((2 * n, 2 * n))
Z = lu.create_var((n, n))
D = lu.create_var((n, n))
# Require that X is symmetric (which implies
# A is symmetric).
# X == X.T
obj, constraints = transpose.graph_implementation([X], (n, n))
constraints.append(lu.create_eq(X, obj))
# Require that X and A are PSD.
constraints += [SDP(X), SDP(A)]
# Fix Z as upper triangular, D as diagonal,
# and diag(D) as diag(Z).
for i in xrange(n):
for j in xrange(n):
if i == j:
# D[i, j] == Z[i, j]
Dij = index.get_index(D, constraints, i, j)
Zij = index.get_index(Z, constraints, i, j)
constraints.append(lu.create_eq(Dij, Zij))
if i != j:
# D[i, j] == 0
Dij = index.get_index(D, constraints, i, j)
constraints.append(lu.create_eq(Dij))
if i > j:
# Z[i, j] == 0
Zij = index.get_index(Z, constraints, i, j)
constraints.append(lu.create_eq(Zij))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == D
index.block_eq(X, D, constraints, 0, n, 0, n)
# X[0:n, n:2*n] == Z,
index.block_eq(X, Z, constraints, 0, n, n, 2 * n)
# X[n:2*n, n:2*n] == A
index.block_eq(X, A, constraints, n, 2 * n, n, 2 * n)
# Add the objective sum(log(D[i, i])
log_diag = []
for i in xrange(n):
Dii = index.get_index(D, constraints, i, i)
obj, constr = log.graph_implementation([Dii], (1, 1))
constraints += constr
log_diag.append(obj)
obj = lu.sum_expr(log_diag)
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
r"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
Notes
-----
Implementation notes.
For general ``p``, the p-norm is equivalent to the following convex inequalities:
.. math::
x_i &\leq r_i\\
-x_i &\leq r_i\\
r_i &\leq s_i^{1/p} t^{1 - 1/p}\\
\sum_i s_i &\leq t,
where :math:`p \geq 1`.
These inequalities are also correct for :math:`p = +\infty` if we interpret :math:`1/\infty` as :math:`0`.
Although the inequalities above are correct, for a few special cases, we can represent the p-norm
more efficiently and with fewer variables and inequalities.
- For :math:`p = 1`, we use the representation
.. math::
x_i &\leq r_i\\
-x_i &\leq r_i\\
\sum_i r_i &\leq t
- For :math:`p = \infty`, we use the representation
.. math::
x_i &\leq t\\
-x_i &\leq t
Note that we don't need the :math:`s` variables or the sum inequality.
- For :math:`p = 2`, we use the natural second-order cone representation
.. math::
\|x\|_2 \leq t
Note that we could have used the set of inequalities given above if we wanted an alternate decomposition
of a large second-order cone into into several smaller inequalities.
"""
p, w = data
x = arg_objs[0]
t = None # dummy value so linter won't complain about initialization
if p != 1:
t = lu.create_var((1, 1))
if p == 2:
return t, [SOC(t, [x])]
if p == np.inf:
r = lu.promote(t, x.size)
else:
r = lu.create_var(x.size)
constraints = [lu.create_geq(lu.sum_expr([x, r])),
lu.create_leq(x, r)]
if p == 1:
return lu.sum_entries(r), constraints
if p == np.inf:
return t, constraints
# otherwise do case of general p
s = lu.create_var(x.size)
# todo: no need to run gm_constr to form the tree each time. we only need to form the tree once
constraints += gm_constrs(r, [s, lu.promote(t, x.size)], w)
constraints += [lu.create_leq(lu.sum_entries(s), t)]
return t, constraints
def graph_implementation(arg_objs, size, data=None):
"""Reduces the atom to an affine expression and list of constraints.
Creates the equivalent problem::
maximize sum(log(D[i, i]))
subject to: D diagonal
diag(D) = diag(Z)
Z is upper triangular.
[D Z; Z.T A] is positive semidefinite
The problem computes the LDL factorization:
.. math::
A = (Z^TD^{-1})D(D^{-1}Z)
This follows from the inequality:
.. math::
\det(A) >= \det(D) + \det([D, Z; Z^T, A])/\det(D)
>= \det(D)
because (Z^TD^{-1})D(D^{-1}Z) is a feasible D, Z that achieves
det(A) = det(D) and the objective maximizes det(D).
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
A = arg_objs[0] # n by n matrix.
n, _ = A.size
X = lu.create_var((2 * n, 2 * n))
Z = lu.create_var((n, n))
D = lu.create_var((n, n))
# Require that X is symmetric (which implies
# A is symmetric).
# X == X.T
obj, constraints = transpose.graph_implementation([X], (n, n))
constraints.append(lu.create_eq(X, obj))
# Require that X and A are PSD.
constraints += [SDP(X), SDP(A)]
# Fix Z as upper triangular, D as diagonal,
# and diag(D) as diag(Z).
for i in xrange(n):
for j in xrange(n):
if i == j:
# D[i, j] == Z[i, j]
Dij = index.get_index(D, constraints, i, j)
Zij = index.get_index(Z, constraints, i, j)
constraints.append(lu.create_eq(Dij, Zij))
if i != j:
# D[i, j] == 0
Dij = index.get_index(D, constraints, i, j)
constraints.append(lu.create_eq(Dij))
if i > j:
# Z[i, j] == 0
Zij = index.get_index(Z, constraints, i, j)
constraints.append(lu.create_eq(Zij))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == D
index.block_eq(X, D, constraints, 0, n, 0, n)
# X[0:n, n:2*n] == Z,
index.block_eq(X, Z, constraints, 0, n, n, 2 * n)
# X[n:2*n, n:2*n] == A
index.block_eq(X, A, constraints, n, 2 * n, n, 2 * n)
# Add the objective sum(log(D[i, i])
log_diag = []
for i in xrange(n):
Dii = index.get_index(D, constraints, i, i)
obj, constr = log.graph_implementation([Dii], (1, 1))
constraints += constr
log_diag.append(obj)
obj = lu.sum_expr(log_diag)
return (obj, constraints)
def graph_implementation(arg_objs, size, data=None):
r"""Reduces the atom to an affine expression and list of constraints.
Parameters
----------
arg_objs : list
LinExpr for each argument.
size : tuple
The size of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
Notes
-----
Implementation notes.
- For general :math:`p \geq 1`, the inequality :math:`\|x\|_p \leq t`
is equivalent to the following convex inequalities:
.. math::
|x_i| &\leq r_i^{1/p} t^{1 - 1/p}\\
\sum_i r_i &= t.
These inequalities happen to also be correct for :math:`p = +\infty`,
if we interpret :math:`1/\infty` as :math:`0`.
- For general :math:`0 < p < 1`, the inequality :math:`\|x\|_p \geq t`
is equivalent to the following convex inequalities:
.. math::
r_i &\leq x_i^{p} t^{1 - p}\\
\sum_i r_i &= t.
- For general :math:`p < 0`, the inequality :math:`\|x\|_p \geq t`
is equivalent to the following convex inequalities:
.. math::
t &\leq x_i^{-p/(1-p)} r_i^{1/(1 - p)}\\
\sum_i r_i &= t.
Although the inequalities above are correct, for a few special cases, we can represent the p-norm
more efficiently and with fewer variables and inequalities.
- For :math:`p = 1`, we use the representation
.. math::
x_i &\leq r_i\\
-x_i &\leq r_i\\
\sum_i r_i &= t
- For :math:`p = \infty`, we use the representation
.. math::
x_i &\leq t\\
-x_i &\leq t
Note that we don't need the :math:`r` variable or the sum inequality.
- For :math:`p = 2`, we use the natural second-order cone representation
.. math::
\|x\|_2 \leq t
Note that we could have used the set of inequalities given above if we wanted an alternate decomposition
of a large second-order cone into into several smaller inequalities.
"""
p = data[0]
x = arg_objs[0]
t = lu.create_var((1, 1))
constraints = []
# first, take care of the special cases of p = 2, inf, and 1
if p == 2:
return t, [SOC(t, [x])]
if p == np.inf:
t_ = lu.promote(t, x.size)
return t, [lu.create_leq(x, t_), lu.create_geq(lu.sum_expr([x, t_]))]
# we need an absolute value constraint for the symmetric convex branches (p >= 1)
# we alias |x| as x from this point forward to make the code pretty :)
if p >= 1:
absx = lu.create_var(x.size)
constraints += [lu.create_leq(x, absx), lu.create_geq(lu.sum_expr([x, absx]))]
x = absx
if p == 1:
return lu.sum_entries(x), constraints
# now, we take care of the remaining convex and concave branches
# to create the rational powers, we need a new variable, r, and
# the constraint sum(r) == t
r = lu.create_var(x.size)
t_ = lu.promote(t, x.size)
constraints += [lu.create_eq(lu.sum_entries(r), t)]
# make p a fraction so that the input weight to gm_constrs
# is a nice tuple of fractions.
p = Fraction(p)
if p < 0:
constraints += gm_constrs(t_, [x, r], (-p / (1 - p), 1 / (1 - p)))
if 0 < p < 1:
constraints += gm_constrs(r, [x, t_], (p, 1 - p))
if p > 1:
constraints += gm_constrs(x, [r, t_], (1 / p, 1 - 1 / p))
return t, constraints