[murbard2]

It's a little strange that they do not have a track that gives gradient information, given that it is often a real world possibility. Also, this basically allows unlimited time between eval... So this becomes a contest about - coming up with a distribution over R^n -> R function - finding the optimal evaluation points to do Bayesian update

I predict the winner will use some a mixture of Gaussian processes with various kernels and stochastic control (with a limited look ahead, otherwise it blows up) to pick the test points.

[pilooch]

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]

You'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]

In 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.