Auto differentiation vs finite differences in optimization toolbox
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Is there a situation where finite differences is faster than automatic differentiation when using the "solve" function call in the optimization toolbox?
I'm using the optimization toolbox to solve an optimization problem with a complex loss funcation and relatively few optimization variables. I'm noticing a substantial speed up when changing the value of "ObjectiveDerivative" from "auto" to "finite-differences."
Any clarification would be greatly appreciated!
回答（1 个）
Alan Weiss
2021-7-11
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Yes, finite differences can be faster than AD. Typically, this occurs in situations like yours where the function or functions are complicated , and the resulting AD expressions are even more complex.
That said, sometimes you can help the solver by setting up your problem in a way that enables solve to operate efficiently. See Create Efficient Optimization Problems and, to a lesser extent for your problem, Separate Optimization Model from Data.
Alan Weiss
MATLAB mathematical toolbox documentation
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2021-7-11
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