I might have missed it, but I can’t see this explained in the guide or the original paper. What is the difference between the “initial” and “final” iterations in ab initio, and what does annealing refer to in this context?
Cheers
Oli
Hi @olibclarke, good question!
When Ab-Initio Reconstruction starts, the proper values of several “hyperparameters” are unknown. Examples of hyperparameters are the learning rate, maximum resolution, noise model, etc.
These hyperparameters are fixed at a starting value during the initial iterations of Ab-Initio Reconstruction. This allows the class volumes to approach a more reasonable starting topology before hyperparametrs are optimized.
Ab-Initio Reconstruction then enters the intermediate iterations, during which hyperparameters “anneal” from their arbitrary starting values to more optimal values determined from the data itself. Annealing describes a process of slowly transitioning from the starting values to the data-determined values rather than jumping directly from one to the other. Ab-Initio Reconstruction uses a dynamic number of intermeidate iterations for this annealing process.
Once the job is using the data-determined hyperparameter values, it does a number of final iterations to optimize the volumes under the new values.
I’ve added the above note to the Ab Initio Reconstruction page as well. I hope that clears it up!
5 Likes
Very helpful, thanks Rich!
1 Like