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We don't (yet), but we are growing rapidly and always looking for ways to help our customers solve optimization problems. Fwiw, we do support categorical parame
by Zephyr314 10y ago
We don't (yet), but we are growing rapidly and always looking for ways to help our customers solve optimization problems. Fwiw, we do support categorical parameters (as well as continuous and integer) and our ensemble of Bayesian optimization techniques are able to solve this mixed type problem much more efficiently than techniques like gradient decent. Although the way we handle the purely combinatorial (only category) problem isn't as flushed out as our mixed type problems. We are looking to grow the team (and our offerings) if you're interested though [1].
[1]: https://sigopt.com/careers https://sigopt.com/careers
- wolfgke 10y ago> [We] are able to solve this mixed type problem much more efficiently than techniques like gradient decent. Naive gradient descent is probably the simplest strategy that one can imagine. How do your algorithms compare to Newton methods for minimizing the primal-dual gap (interior point methods)?
- Zephyr314 10y agoWe compare to some standard convex optimization techniques implemented in scipy here [1]. We have comparisons to some other Bayesian methods here [2]. I'm happy to answer any questions! [1]: https://github.com/sigopt/sigopt-examples/blob/master/ipython-notebook-example/SigOpt_Introduction.ipynb https://github.com/sigopt/sigopt-examples/blob/master/ipytho... [2]: http://arxiv.org/abs/1603.09441 http://arxiv.org/abs/1603.09441