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Hi @hvarfner, Thanks so much for your reply. Sure, below is a minimal version of my code to reproduce the issue. # --- Objective function-Branin2d--- # import math import numpy...

Thank you for the suggestion ! I overrode condition_on_observations() as you advised (code below), but I’m now running into the following error : - Output shape not equal to that...

Hi, any help or guidance to fix the above issue would be greatly appreciated !

Hi @hvarfner, thank you for the suggestion. I’ll look into it. Just one more thing I wanted to ask: I managed to get my qKnowledgeGradient code working, but I’m hitting...

To be precise, with raw samples = 20, num_fantasies = 32, q (in optimise_acqf) = 1, num_samples=256, thinning=16, num_restarts=3, I get the following error (with some print statements for debugging)...

Thank you so much @saitcakmak for clearing my confusion. I got some useful insights from that post. However, now I am encountering another memory error in matern_kernel.py (on increasing raw...

Hi @saitcakmak , thank you for your reply. The dimension 1611 arises because I consider 32 fantasy scenarios for each of the 50 candidate points (i.e. raw samples), which results...

@saitcakmak Yes, I realised I was mishandling the shape in condition_on_observation. The initial shapes I receive in condition_on_observation are- ``` X.shape = [raw_samples, 1 , d] Y.shape = [num_fantasies, raw_samples,...

@saitcakmak Thank you for the suggestions. I think the value function which is `PosteriorMean` in my case, does return `[fantasies, raw_samples, mcmc]` but it also has the `@t_batch_mode_transform ` decorator...

Okay, thank you so much for your help!