Skip to content

Use psd_safe_cholesky in MES #518

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Use psd_safe_cholesky in MES #518

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 2 additions & 1 deletion botorch/acquisition/max_value_entropy_search.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@
from botorch.models.utils import check_no_nans
from botorch.sampling.samplers import SobolQMCNormalSampler
from botorch.utils.transforms import match_batch_shape, t_batch_mode_transform
from gpytorch.utils.cholesky import psd_safe_cholesky
from scipy.optimize import brentq
from torch import Tensor

Expand Down Expand Up @@ -240,7 +241,7 @@ def _compute_information_gain(
# compute mean and std for fM|ym, x, Dt ~ N(u, s^2)
samples_m = self.weight * self.sampler(posterior_m).squeeze(-1)
# s_m x batch_shape x num_fantasies x (1 + num_trace_observations)
L = torch.cholesky(variance_m)
L = psd_safe_cholesky(variance_m)
temp_term = torch.cholesky_solve(covar_mM.unsqueeze(-1), L).transpose(-2, -1)
# equivalent to torch.matmul(covar_mM.unsqueeze(-2), torch.inverse(variance_m))
# batch_shape x num_fantasies x 1 x (1 + num_trace_observations)
Expand Down