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You can compute the gradient, it just has a high budget cost. The usual winner is a flavor of CMA-ES, though they may have picked up the functions to avoid thi
by pilooch 11y ago
You can compute the gradient, it just has a high budget cost.
The usual winner is a flavor of CMA-ES, though they may have picked up the functions to avoid this.
- murbard2 11y agoYou're missing my point. In many real world problems, it is cheap to compute the gradient. Thus, black box optimization methods which can use gradient information are inherently valuable, and it is surprising that they do not have a track that would allow showcasing those.
- nullc 11y agoIn a great many real world problems, including most of the most expensive ones gradients are _not_ available, or can only be expensively computable... even if your objective is differentiable, automatic differentiation isn't cheap on non-trivial functions. Experiences differ, but in mine the most common place to find objectives with gradients is in optimizer challenges. That said; sure, there should be a track that gives you the gradients. I agree that it would be nice if there were another track.