WIP: slimmed-down Levenberg-Marquardt for nonlinear least squares #500
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…copy-paste of custom_leastsq to simplish_lstsq)
…ty" in the CustomLSOptimizer behavior. Remove damping_clip as well, since that was only used for damping_mode != "identity".
…tomLMOptimizer when damping_basis=="diagonal_basis". I believe that since I already restricted damping_mode=identity in the previous commit that damping_basis has no special meaning anyway.
…ored out into their own functions
…s in simplerlm.py instead of customlm.py, so we can mark the whole customlm.py file as deprecated.
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pyGSTi's nonlinear least squares solver is a custom implementation with Levenberg-Marquardt with a dizzying array of options. The core algorithm is over 800 lines long and has 10 levels of indentation at its deepest point. The complexity of this existing implementation makes it basically impossible to extend.
This PR introduces slimmed-down infrastructure for nonlinear least squares. I started by copying customlm.py into a new file called simplerlm.py, then I removed one feature at a time in a series of commits. "Removal" of a feature meant making the default behavior for a given option the only behavior. I haven't tested these changes yet but they should work just fine.
This PR has two goals.
Notes
I tried to use ChatGPT to help split the monolithic custom_leastsq function in customlm.py into simpler functions. This turned out to not work for reasons that I'll discuss off-GitHub if people are interested.
For the curious, I'm doing this because I want to extend our nonlinear least squares solver in two ways. First, I want it to support pytorch Tensors; right now we only support numpy arrays and whatever home-cooked thing we have for distributed-memory computations. Second, I want to experiment with optimization algorithms that rarely (if ever!) require evaluating the full Jacobian of circuit outcome probabilities.