Python torch.potrf() Examples
The following are 8
code examples of torch.potrf().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
torch
, or try the search function
.
.
Example #1
| Source File: models.py From optnet with Apache License 2.0 | 6 votes |
|
def __init__(self, nFeatures, args):
super().__init__()
nHidden, neq, nineq = 2*nFeatures-1,0,2*nFeatures-2
assert(neq==0)
# self.fc1 = nn.Linear(nFeatures, nHidden)
self.M = Variable(torch.tril(torch.ones(nHidden, nHidden)).cuda())
Q = 1e-8*torch.eye(nHidden)
Q[:nFeatures,:nFeatures] = torch.eye(nFeatures)
self.L = Variable(torch.potrf(Q))
self.D = Parameter(0.3*torch.randn(nFeatures-1, nFeatures))
# self.lam = Parameter(20.*torch.ones(1))
self.h = Variable(torch.zeros(nineq))
self.nFeatures = nFeatures
self.nHidden = nHidden
self.neq = neq
self.nineq = nineq
self.args = args
Example #2
| Source File: gplvm.py From pmf-automl with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def get_cov(self, ix=None):
if ix is None:
ix = torch.arange(0, self.N)
return torch.potrf(self.kernel(self.X[ix])
+ torch.eye(ix.numel())
*transform_forward(self.variance),
upper=False)
Example #3
| Source File: single.py From qpth with Apache License 2.0 | 5 votes |
|
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
Example #4
| Source File: single.py From qpth with Apache License 2.0 | 5 votes |
|
def factor_kkt(U_S, R, d):
""" Factor the U22 block that we can only do after we know D. """
nineq = R.size(0)
U_S[-nineq:, -nineq:] = torch.potrf(R + torch.diag(1 / d.cpu()).type_as(d))
Example #5
| Source File: single.py From qpth with Apache License 2.0 | 5 votes |
|
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
Example #6
| Source File: pytorch.py From deepx with MIT License | 5 votes |
|
def cholesky(self, A, lower=True, warn=False, correct=True):
return torch.potrf(A, upper=not lower)
# Tensorflow interface
Example #7
| Source File: gp.py From GPPVAE with Apache License 2.0 | 5 votes |
|
def U_UBi_Shb(self, Vs, vs):
# compute U and V
V = torch.cat([torch.sqrt(vs[i]) * V for i, V in enumerate(Vs)], 1)
U = V / torch.sqrt(vs[-1])
eye = torch.eye(U.shape[1]).cuda()
B = torch.mm(torch.transpose(U, 0, 1), U) + eye
# cholB = torch.potrf(B, upper=False)
# Bi = torch.potri(cholB, upper=False)
Ub, Shb, Vb = torch.svd(B)
# Bi = (Vb / Shb).mm(torch.transpose(Vb, 0, 1))
Bi = torch.inverse(B)
UBi = torch.mm(U, Bi)
return U, UBi, Shb
Example #8
| Source File: models.py From optnet with Apache License 2.0 | 4 votes |
|
def __init__(self, nFeatures, args):
super(OptNet, self).__init__()
nHidden, neq, nineq = 2*nFeatures-1,0,2*nFeatures-2
assert(neq==0)
self.fc1 = nn.Linear(nFeatures, nHidden)
self.M = Variable(torch.tril(torch.ones(nHidden, nHidden)).cuda())
if args.tvInit:
Q = 1e-8*torch.eye(nHidden)
Q[:nFeatures,:nFeatures] = torch.eye(nFeatures)
self.L = Parameter(torch.potrf(Q))
D = torch.zeros(nFeatures-1, nFeatures)
D[:nFeatures-1,:nFeatures-1] = torch.eye(nFeatures-1)
D[:nFeatures-1,1:nFeatures] -= torch.eye(nFeatures-1)
G_ = block((( D, -torch.eye(nFeatures-1)),
(-D, -torch.eye(nFeatures-1))))
self.G = Parameter(G_)
self.s0 = Parameter(torch.ones(2*nFeatures-2)+1e-6*torch.randn(2*nFeatures-2))
G_pinv = (G_.t().mm(G_)+1e-5*torch.eye(nHidden)).inverse().mm(G_.t())
self.z0 = Parameter(-G_pinv.mv(self.s0.data)+1e-6*torch.randn(nHidden))
lam = 21.21
W_fc1, b_fc1 = self.fc1.weight, self.fc1.bias
W_fc1.data[:,:] = 1e-3*torch.randn((2*nFeatures-1, nFeatures))
# W_fc1.data[:,:] = 0.0
W_fc1.data[:nFeatures,:nFeatures] += -torch.eye(nFeatures)
# b_fc1.data[:] = torch.zeros(2*nFeatures-1)
b_fc1.data[:] = 0.0
b_fc1.data[nFeatures:2*nFeatures-1] = lam
else:
self.L = Parameter(torch.tril(torch.rand(nHidden, nHidden)))
self.G = Parameter(torch.Tensor(nineq,nHidden).uniform_(-1,1))
self.z0 = Parameter(torch.zeros(nHidden))
self.s0 = Parameter(torch.ones(nineq))
self.nFeatures = nFeatures
self.nHidden = nHidden
self.neq = neq
self.nineq = nineq
self.args = args
