Memory efficiency of Cashvalue_ME_EX4

[MatthewCaseres]

Code like this seems to be having impacts on memory efficiency

    cl = np.array(list(claims(t, kind) for t in range(max_proj_len()))).transpose()

    return cl @ disc_factors()[:max_proj_len()]

I imagine that the logic described in this blog post (https://modelx.io/blog/2022/03/26/running-model-while-saving-memory/) will allow for some slight modifications to be made so that the memory efficiency is much improved.

[MatthewCaseres]

Did some more reading and I see that the blog post linked above reports that -

The maximum additional consumption is less then 9 GB, 30% of the original run, and the run time is actually shorter than the original run.

The implementation of the model appears to be O(P*T) where P=policies and T=timesteps. It might be possible to achieve savings of over 100x on memory by making the memory complexity O(P) by changing the way the calculation is done?

I am trying to make some publication for the benchmarks repository in a journal like Journal of Open Source Software. It will be discussing the memory and time complexity of actuarial modeling and we are focusing on the basic term and cashvalue_ME_EX4 models. A coauthor reports that lifelib is consuming more memory than Julia, and I believe it is related to this issue.