本文整理汇总了Python中torch.potrs方法的典型用法代码示例。如果您正苦于以下问题：Python torch.potrs方法的具体用法？Python torch.potrs怎么用？Python torch.potrs使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch的用法示例。
在下文中一共展示了torch.potrs方法的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_potrs
# 需要导入模块: import torch [as 别名]
# 或者: from torch import potrs [as 别名]
def test_potrs(self):
a=torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
b=torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03),
(-1.56, 4.00, -8.67, 1.75, 2.86),
(9.81, -4.09, -4.57, -8.61, 8.99))).t()
# make sure 'a' is symmetric PSD
a = torch.mm(a, a.t())
# upper Triangular Test
U = torch.potrf(a)
x = torch.potrs(b, U)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
# lower Triangular Test
L = torch.potrf(a, False)
x = torch.potrs(b, L, False)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12) 示例2: test_potrs
# 需要导入模块: import torch [as 别名]
# 或者: from torch import potrs [as 别名]
def test_potrs(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03),
(-1.56, 4.00, -8.67, 1.75, 2.86),
(9.81, -4.09, -4.57, -8.61, 8.99))).t()
# make sure 'a' is symmetric PSD
a = torch.mm(a, a.t())
# upper Triangular Test
U = torch.potrf(a)
x = torch.potrs(b, U)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
# lower Triangular Test
L = torch.potrf(a, False)
x = torch.potrs(b, L, False)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12) 示例3: solve_kkt
# 需要导入模块: import torch [as 别名]
# 或者: from torch import potrs [as 别名]
def solve_kkt(U_Q, d, G, A, U_S, rx, rs, rz, ry, dbg=False):
""" Solve KKT equations for the affine step"""
nineq, nz, neq, _ = get_sizes(G, A)
invQ_rx = torch.potrs(rx.view(-1, 1), U_Q).view(-1)
if neq > 0:
h = torch.cat([torch.mv(A, invQ_rx) - ry,
torch.mv(G, invQ_rx) + rs / d - rz], 0)
else:
h = torch.mv(G, invQ_rx) + rs / d - rz
w = -torch.potrs(h.view(-1, 1), U_S).view(-1)
g1 = -rx - torch.mv(G.t(), w[neq:])
if neq > 0:
g1 -= torch.mv(A.t(), w[:neq])
g2 = -rs - w[neq:]
dx = torch.potrs(g1.view(-1, 1), U_Q).view(-1)
ds = g2 / d
dz = w[neq:]
dy = w[:neq] if neq > 0 else None
# if np.all(np.array([x.norm() for x in [rx, rs, rz, ry]]) != 0):
if dbg:
import IPython
import sys
IPython.embed()
sys.exit(-1)
# if rs.norm() > 0: import IPython, sys; IPython.embed(); sys.exit(-1)
return dx, ds, dz, dy 示例4: pre_factor_kkt
# 需要导入模块: import torch [as 别名]
# 或者: from torch import potrs [as 别名]
def pre_factor_kkt(Q, G, A):
""" Perform all one-time factorizations and cache relevant matrix products"""
nineq, nz, neq, _ = get_sizes(G, A)
# S = [ A Q^{-1} A^T A Q^{-1} G^T ]
# [ G Q^{-1} A^T G Q^{-1} G^T + D^{-1} ]
U_Q = torch.potrf(Q)
# partial cholesky of S matrix
U_S = torch.zeros(neq + nineq, neq + nineq).type_as(Q)
G_invQ_GT = torch.mm(G, torch.potrs(G.t(), U_Q))
R = G_invQ_GT
if neq > 0:
invQ_AT = torch.potrs(A.t(), U_Q)
A_invQ_AT = torch.mm(A, invQ_AT)
G_invQ_AT = torch.mm(G, invQ_AT)
# TODO: torch.potrf sometimes says the matrix is not PSD but
# numpy does? I filed an issue at
# https://github.com/pytorch/pytorch/issues/199
try:
U11 = torch.potrf(A_invQ_AT)
except:
U11 = torch.Tensor(np.linalg.cholesky(
A_invQ_AT.cpu().numpy())).type_as(A_invQ_AT)
# TODO: torch.trtrs is currently not implemented on the GPU
# and we are using gesv as a workaround.
U12 = torch.gesv(G_invQ_AT.t(), U11.t())[0]
U_S[:neq, :neq] = U11
U_S[:neq, neq:] = U12
R -= torch.mm(U12.t(), U12)
return U_Q, U_S, R 示例5: factor_solve_kkt
# 需要导入模块: import torch [as 别名]
# 或者: from torch import potrs [as 别名]
def factor_solve_kkt(Q, D, G, A, rx, rs, rz, ry):
nineq, nz, neq, _ = get_sizes(G, A)
if neq > 0:
H_ = torch.cat([torch.cat([Q, torch.zeros(nz, nineq).type_as(Q)], 1),
torch.cat([torch.zeros(nineq, nz).type_as(Q), D], 1)], 0)
A_ = torch.cat([torch.cat([G, torch.eye(nineq).type_as(Q)], 1),
torch.cat([A, torch.zeros(neq, nineq).type_as(Q)], 1)], 0)
g_ = torch.cat([rx, rs], 0)
h_ = torch.cat([rz, ry], 0)
else:
H_ = torch.cat([torch.cat([Q, torch.zeros(nz, nineq).type_as(Q)], 1),
torch.cat([torch.zeros(nineq, nz).type_as(Q), D], 1)], 0)
A_ = torch.cat([G, torch.eye(nineq).type_as(Q)], 1)
g_ = torch.cat([rx, rs], 0)
h_ = rz
U_H_ = torch.potrf(H_)
invH_A_ = torch.potrs(A_.t(), U_H_)
invH_g_ = torch.potrs(g_.view(-1, 1), U_H_).view(-1)
S_ = torch.mm(A_, invH_A_)
U_S_ = torch.potrf(S_)
t_ = torch.mv(A_, invH_g_).view(-1, 1) - h_
w_ = -torch.potrs(t_, U_S_).view(-1)
v_ = torch.potrs(-g_.view(-1, 1) - torch.mv(A_.t(), w_), U_H_).view(-1)
return v_[:nz], v_[nz:], w_[:nineq], w_[nineq:] if neq > 0 else None