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If you wish to save things during an optimisation run (typically the loss trace, but sometimes other stuff as well) you can typically implement this during a callback and your own structure. Depending on situations this can be more or less messy.
However, it would be straightforward to have an argument to solvesave_state(os::OptimizationState) -> u (potentially one would want to impose some conditions on u). Next, in each step (or every n steps), save_state is called on the optimisation state and saves the result to a vector (state_trajectory) which is saved as a field of the OptimizationSoltution.
By default, save_state would do nothing and state_trajectory would be nothing.
The text was updated successfully, but these errors were encountered:
If you wish to save things during an optimisation run (typically the loss trace, but sometimes other stuff as well) you can typically implement this during a callback and your own structure. Depending on situations this can be more or less messy.
However, it would be straightforward to have an argument to
solve
save_state(os::OptimizationState) -> u
(potentially one would want to impose some conditions onu
). Next, in each step (or every n steps),save_state
is called on the optimisation state and saves the result to a vector (state_trajectory
) which is saved as a field of theOptimizationSoltution
.By default,
save_state
would do nothing andstate_trajectory
would benothing
.The text was updated successfully, but these errors were encountered: