StochasticOptimization.jl
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JuliaML
Implementations of stochastic optimization algorithms and solvers
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The `Iteration` sub-module exports an `ObsIterator` type that clashes with `LearnBase`. This results in the unit tests currently failing with: ``` julia> Pkg.test("StochasticOptimization") INFO: Computing test dependencies for StochasticOptimization... INFO:...
This package is really useful as learning rate updaters. I'm using a variant of the Adam scheme here for SGD. I think it is unnecessary to have \rho_i^t as vectors....
This is an interesting stochastic optimizer with some nice theoretical guarantees for convex problems. Would be interesting to compare to the others we have implemented already. https://papers.nips.cc/paper/4937-accelerating-stochastic-gradient-descent-using-predictive-variance-reduction.pdf
Frequently it's not enough to have an in-memory representation of a model or the optimization algorithm state. We want to serialize the structure, parameters, and state of the models and...
I think it would be smart to attempt to use the great tools already created if they can fit our needs, or improved to fit our needs. Here's a summary...