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Part 2. could look roughly the same in many algorithms, with the difference being what happens inside each iteration.
For this, an iteration interface could be nice. This also lets the user debug, inspect and so on without us having to anticipate all they might want to inspect at each iteration. The top-level functions can be kept as-is, but users with high-perf will just do the setup and iterations part, without allocating or logging.
Most methods consist in
Part 2. could look roughly the same in many algorithms, with the difference being what happens inside each iteration.
For this, an iteration interface could be nice. This also lets the user debug, inspect and so on without us having to anticipate all they might want to inspect at each iteration. The top-level functions can be kept as-is, but users with high-perf will just do the setup and iterations part, without allocating or logging.
Resources:
This discussion mentions the approach in Manopt.jl:
https://discourse.julialang.org/t/ann-optimkit-jl-a-blissfully-ignorant-julia-package-for-gradient-optimization/41063
A blog post describes the iterator approach to solve a linear system:
https://lostella.github.io/2018/07/25/iterative-methods-done-right.html
This is not high-priority, but can make FW more flexible and let users decide what they want to log and how
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