High memory usage during the fix step

[SquirrelKnight]

I am using data from two different surveys at one site. The initial procedure works well using Log_Lambda0 = -1 and mu_t = 0.0. For the fix step, higher mu_t values (in this case 0.5) leads to a program failure due too much memory usage. I have tracked this down to the data correlation step, specifically with lu.solve in line 263 of setup_model.py. I am uncertain if there is a more efficient/less memory intensive way of addressing this issue. Here is the information for matrix E:

<66864x66864 sparse matrix of type '<class 'numpy.float64'>'
with 4331926 stored elements in List of Lists format>

Currently, my RAM is limited to 64 gb. I would be happy to pack the data into a zip file and provide it through Google Drive, if you would like to use it for a test case. I understand if this an impossible issue at the moment, and I greatly appreciate your time.