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))
constraints = []
# 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)
half = lu.create_const(0.5, (1, 1))
trace = lu.mul_expr(half, lu.trace(X), (1, 1))
# Add SDP constraint.
return (trace, [SDP(X)] + 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))
constraints = []
# 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)
half = lu.create_const(0.5, (1, 1))
trace = lu.mul_expr(half, lu.trace(X), (1, 1))
# Add SDP constraint.
return (trace, [SDP(X)] + constraints)
def graph_implementation(arg_objs, size, data=None):
"""Stack the expressions horizontally.
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 = lu.create_var(size)
constraints = []
# Create an equality constraint for each arg.
offset = 0
for arg in arg_objs:
index.block_eq(X, arg, constraints,
0, size[0],
offset, arg.size[1] + offset)
offset += arg.size[1]
return (X, constraints)
def graph_implementation(arg_objs, size, data=None):
"""Stack the expressions vertically.
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 = lu.create_var(size)
constraints = []
# Create an equality constraint for each arg.
offset = 0
for arg in arg_objs:
index.block_eq(X, arg, constraints,
offset, arg.size[0] + offset,
0, size[1])
offset += arg.size[0]
return (X, 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 by m matrix.
n, m = A.size
# Create a matrix with Schur complement I*t - (1/t)*A.T*A.
X = lu.create_var((n + m, n + m))
t = lu.create_var((1, 1))
constraints = []
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == I_n*t
prom_t = lu.promote(t, (n, 1))
index.block_eq(X, lu.diag_vec(prom_t), constraints, 0, n, 0, n)
# X[0:n, n:n+m] == A
index.block_eq(X, A, constraints, 0, n, n, n + m)
# X[n:n+m, n:n+m] == I_m*t
prom_t = lu.promote(t, (m, 1))
# prom_t = lu.promote(lu.create_const(1, (1,1)), (m, 1))
index.block_eq(X, lu.diag_vec(prom_t), constraints, n, n + m, n, n + m)
# Add SDP constraint.
return (t, constraints + [SDP(X)])
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] # n by m matrix.
P = arg_objs[1] # n by n matrix.
n, m = X.size
# Create a matrix with Schur complement T - X.T*P^-1*X.
M = lu.create_var((n + m, n + m))
T = lu.create_var((m, m))
constraints = []
# Fix M using the fact that P must be affine by the DCP rules.
# M[0:n, 0:n] == P.
index.block_eq(M, P, constraints, 0, n, 0, n)
# M[0:n, n:n+m] == X
index.block_eq(M, X, constraints, 0, n, n, n + m)
# M[n:n+m, n:n+m] == T
index.block_eq(M, T, constraints, n, n + m, n, n + m)
# Add SDP constraint.
return (lu.trace(T), constraints + [SDP(M)])
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]
P = arg_objs[1] # n by n matrix.
n, _ = P.size
# Create a matrix with Schur complement t - x.T*P^-1*x.
M = lu.create_var((n + 1, n + 1))
t = lu.create_var((1, 1))
constraints = []
# Fix M using the fact that P must be affine by the DCP rules.
# M[0:n, 0:n] == P.
index.block_eq(M, P, constraints, 0, n, 0, n)
# M[0:n, n:n+1] == x
index.block_eq(M, x, constraints, 0, n, n, n + 1)
# M[n:n+1, n:n+1] == t
index.block_eq(M, t, constraints, n, n + 1, n, n + 1)
# Add SDP constraint.
return (t, constraints + [SDP(M)])
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] # m by n matrix.
n, m = 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((n+m, n+m))
# Expand A.T.
obj, constraints = transpose.graph_implementation([A], (m, n))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n,n:n+m] == A
index.block_eq(X, A, constraints,
0, n, n, n+m)
# X[n:n+m,0:n] == obj
index.block_eq(X, obj, constraints,
n, n+m, 0, n)
diag = [index.get_index(X, constraints, i, i) for i in range(n+m)]
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 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] # m by n matrix.
n, m = 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((n + m, n + m))
# Expand A.T.
obj, constraints = transpose.graph_implementation([A], (m, n))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n,n:n+m] == A
index.block_eq(X, A, constraints, 0, n, n, n + m)
# X[n:n+m,0:n] == obj
index.block_eq(X, obj, constraints, n, n + m, 0, n)
diag = [index.get_index(X, constraints, i, i) for i in range(n + m)]
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 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] # m by n matrix.
n, m = A.size
# Create a matrix with Schur complement I*t - (1/t)*A.T*A.
X = lu.create_var((n+m, n+m))
t = lu.create_var((1, 1))
I_n = lu.create_const(sp.eye(n), (n, n))
I_m = lu.create_const(sp.eye(m), (m, m))
# Expand A.T.
obj, constraints = transpose.graph_implementation([A], (m, n))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == I_n*t
index.block_eq(X, lu.mul_expr(I_n, t, (n, n)), constraints,
0, n, 0, n)
# X[0:n, n:n+m] == A
index.block_eq(X, A, constraints,
0, n, n, n+m)
# X[n:n+m, 0:n] == obj
index.block_eq(X, obj, constraints,
n, n+m, 0, n)
# X[n:n+m, n:n+m] == I_m*t
index.block_eq(X, lu.mul_expr(I_m, t, (m, m)), constraints,
n, n+m, n, n+m)
# Add SDP constraint.
return (t, constraints + [SDP(X)])
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 by m matrix.
n, m = A.size
# Create a matrix with Schur complement I*t - (1/t)*A.T*A.
X = lu.create_var((n+m, n+m))
t = lu.create_var((1, 1))
constraints = []
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == I_n*t
prom_t = lu.promote(t, (n, 1))
index.block_eq(X, lu.diag_vec(prom_t), constraints,
0, n, 0, n)
# X[0:n, n:n+m] == A
index.block_eq(X, A, constraints,
0, n, n, n+m)
# X[n:n+m, n:n+m] == I_m*t
prom_t = lu.promote(t, (m, 1))
# prom_t = lu.promote(lu.create_const(1, (1,1)), (m, 1))
index.block_eq(X, lu.diag_vec(prom_t), constraints,
n, n+m, n, n+m)
# Add SDP constraint.
return (t, constraints + [SDP(X)])
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] # n by m matrix.
P = arg_objs[1] # n by n matrix.
n, m = X.size
# Create a matrix with Schur complement T - X.T*P^-1*X.
M = lu.create_var((n + m, n + m))
T = lu.create_var((m, m))
constraints = []
# Fix M using the fact that P must be affine by the DCP rules.
# M[0:n, 0:n] == P.
index.block_eq(M, P, constraints,
0, n, 0, n)
# M[0:n, n:n+m] == X
index.block_eq(M, X, constraints,
0, n, n, n+m)
# M[n:n+m, n:n+m] == T
index.block_eq(M, T, constraints,
n, n+m, n, n+m)
# Add SDP constraint.
return (lu.trace(T), constraints + [SDP(M)])
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]
P = arg_objs[1] # n by n matrix.
n, _ = P.size
# Create a matrix with Schur complement t - x.T*P^-1*x.
M = lu.create_var((n + 1, n + 1))
t = lu.create_var((1, 1))
constraints = []
# Fix M using the fact that P must be affine by the DCP rules.
# M[0:n, 0:n] == P.
index.block_eq(M, P, constraints,
0, n, 0, n)
# M[0:n, n:n+1] == x
index.block_eq(M, x, constraints,
0, n, n, n+1)
# M[n:n+1, n:n+1] == t
index.block_eq(M, t, constraints,
n, n+1, n, n+1)
# Add SDP constraint.
return (t, constraints + [SDP(M)])
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))
X, constraints = Semidef(2 * n).canonical_form
Z = lu.create_var((n, n))
D = lu.create_var((n, 1))
# Require that X and A are PSD.
constraints += [SDP(A)]
# Fix Z as upper triangular, D as diagonal,
# and diag(D) as diag(Z).
Z_lower_tri = lu.upper_tri(lu.transpose(Z))
constraints.append(lu.create_eq(Z_lower_tri))
# D[i, i] = Z[i, i]
constraints.append(lu.create_eq(D, lu.diag_mat(Z)))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == D
index.block_eq(X, lu.diag_vec(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])
obj, constr = log.graph_implementation([D], (n, 1))
return (lu.sum_entries(obj), constraints + constr)
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, 1))
# 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).
Z_lower_tri = lu.upper_tri(lu.transpose(Z))
constraints.append(lu.create_eq(Z_lower_tri))
# D[i, i] = Z[i, i]
constraints.append(lu.create_eq(D, lu.diag_mat(Z)))
# Fix X using the fact that A must be affine by the DCP rules.
# X[0:n, 0:n] == D
index.block_eq(X, lu.diag_vec(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])
obj, constr = log.graph_implementation([D], (n, 1))
return (lu.sum_entries(obj), constraints + constr)
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):
"""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)
