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]
axis = data[0]
t = lu.create_var(size)
# sum(exp(x - t)) <= 1
if axis is None:
prom_t = lu.promote(t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
obj = lu.sum_entries(obj)
elif axis == 0:
prom_size = (x.size[0], 1)
ones = lu.create_const(np.ones(prom_size), prom_size)
prom_t = lu.mul_expr(ones, t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
const_size = (1, x.size[0])
ones = lu.create_const(np.ones(const_size), const_size)
obj = lu.mul_expr(ones, obj, size)
else: # axis == 1
prom_size = (1, x.size[1])
ones = lu.create_const(np.ones(prom_size), prom_size)
prom_t = lu.rmul_expr(t, ones, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
const_size = (x.size[1], 1)
ones = lu.create_const(np.ones(const_size), const_size)
obj = lu.rmul_expr(obj, ones, size)
ones = lu.create_const(np.ones(size), size)
constraints += [lu.create_leq(obj, ones)]
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]
axis = data[0]
t = lu.create_var(size)
# sum(exp(x - t)) <= 1
if axis is None:
prom_t = lu.promote(t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
obj = lu.sum_entries(obj)
elif axis == 0:
prom_size = (x.size[0], 1)
ones = lu.create_const(np.ones(prom_size), prom_size)
prom_t = lu.mul_expr(ones, t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
const_size = (1, x.size[0])
ones = lu.create_const(np.ones(const_size), const_size)
obj = lu.mul_expr(ones, obj, size)
else: # axis == 1
prom_size = (1, x.size[1])
ones = lu.create_const(np.ones(prom_size), prom_size)
prom_t = lu.rmul_expr(t, ones, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
const_size = (x.size[1], 1)
ones = lu.create_const(np.ones(const_size), const_size)
obj = lu.rmul_expr(obj, ones, size)
ones = lu.create_const(np.ones(size), size)
constraints += [lu.create_leq(obj, ones)]
return (t, 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 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)
"""
A = arg_objs[0]
n, _ = A.size
# Requires that A is symmetric.
obj, constraints = transpose.graph_implementation([A], (n, n))
# A == A.T
constraints.append(lu.create_eq(A, obj))
# SDP constraint.
t = lu.create_var((1, 1))
prom_t = lu.promote(t, (n, 1))
# I*t - A
expr = lu.sub_expr(A, lu.diag_vec(prom_t))
return (t, [SDP(expr)] + 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)
"""
# 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)
"""
A = arg_objs[0]
n, _ = A.size
# Requires that A is symmetric.
obj, constraints = transpose.graph_implementation([A], (n, n))
# A == A.T
constraints.append(lu.create_eq(A, obj))
# SDP constraint.
t = lu.create_var((1, 1))
prom_t = lu.promote(t, (n, 1))
# I*t - A
expr = lu.sub_expr(A, lu.diag_vec(prom_t))
return (t, [SDP(expr)] + 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]
t = lu.create_var((1, 1))
# sum(exp(x - t))
prom_t = lu.promote(t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
obj, constr = sum_entries.graph_implementation([obj], (1, 1))
# obj <= 1
one = lu.create_const(1, (1, 1))
constraints += constr + [lu.create_leq(obj, one)]
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] # 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)
"""
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)
"""
A = arg_objs[0]
n, _ = A.size
# SDP constraint.
t = lu.create_var((1, 1))
prom_t = lu.promote(t, (n, 1))
# I*t - A
expr = lu.sub_expr(lu.diag_vec(prom_t), A)
return (t, [SDP(expr)])
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 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)
"""
# 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((1, 1))
# sum(exp(x - t))
prom_t = lu.promote(t, x.size)
expr = lu.sub_expr(x, prom_t)
obj, constraints = exp.graph_implementation([expr], x.size)
obj, constr = sum_entries.graph_implementation([obj], (1, 1))
# obj <= 1
one = lu.create_const(1, (1, 1))
constraints += constr + [lu.create_leq(obj, one)]
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]
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 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 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 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 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 gm(t, x, y):
return SOC_Elemwise(lu.sum_expr([x, y]),
[lu.sub_expr(x, y),
lu.mul_expr(two, t, t.size)])
