Download to read offline
Uploaded byOlivier Teytaud
Versatile Black-box Optimization
This document discusses a new algorithm called SHIWA that aims to be an "aggregate" or unified approach for black-box optimization problems, analogous to the fictional cosmic entity Cthulhu being capable of everything. SHIWA is designed to automatically select the most appropriate optimization method for a given black-box problem, whether continuous or discrete, noisy or noise-free, and can evaluate solutions sequentially or in parallel. The algorithm is part of the open-source Nevergrad Python library and has been tested on a variety of benchmarks.
Versatile Black-box Optimization
- 1. Open scalable genericZero learning: Polygames @ FB. Tristan Cazenave, Univ. Dauphine Yen-Chi Chen, National Taiwan Normal University Guan-Wei Chen, National Dong Hwa University Shi-Yu Chen, National Dong Hwa University Xian-Dong Chiu, National Dong Hwa University Julien Dehos, Univ. Littoral Cote d’Opale Maria Elsa, National Dong Hwa University Qucheng Gong, Facebook AI Research Hengyuan Hu, Facebook AI Research Vasil Khalidov, Facebook AI Research Chen-Ling Li, National Dong Hwa University Hsin-I Lin, National Dong Hwa University Yu-Jin Lin, National Dong Hwa University . 2019 Contact: oteytaud@fb.com Xavier Martinet, Facebook AI Research Vegard Mella, Facebook AI Research Jeremy Rapin, Facebook AI Research Baptiste Roziere, Facebook AI Research Gabriel Synnaeve, Facebook AI Research Fabien Teytaud, Univ. Littoral Cote d’Opale Olivier Teytaud, Facebook AI Research Shi-Cheng Ye, National Dong Hwa University Yi-Jun Ye, National Dong Hwa University Shi-Jim Yen, National Dong Hwa University Sergey Zagoruyko, Facebook AI Research
- 2. Not all algorithmsare good at everything Apr. 2019 Contact: oteytaud@fb.com Good at punching (31 m/s, twice faster than any karateka) Good at looking: sees a rabbit moving at 2 km, vision 270 degrees Tardigrada: good at surviving (there are probably some of them alive on the moon) Only an aggregate like Cthulhu is good at everything ! Let’s do the CTHULHU of black-box optimization !
- 3. We don’t knowthe objective function, because it’s black box. But we know: - The type of variables (discrete/continuous ?) - If there is noise - The number of variables - The budget (number of function evaluations) - The parallelism (how many simultaneous cases) Our Cthulhu algorithm is termed SHIWA !
- 4. You helped usso much! A very collective Cthulhu.
- 5. Works on goodall artificial stuff: YABBOB
- 6. Works on RealWorld stuff: Nevergrad’s various benchmarks
- 7. A very simpleidea: automatize and systematize the work of a scientist deciding which method should be applied on which test case. Works on noisy-free or noisy cases. Works on continuous or discrete. Sequential or parallel. A unified test on a stable, generic, big platform (dozens of experiments, see paper): NEVERGRAD (python, open source on github, maintained, readable!) Join us: Facebook group ”Nevergrad Users” I’m pretty sure your algorithm can help Shiwa.
- 8. Join us: Facebook group”Nevergrad Users” I’m pretty sure your algorithm can help Shiwa. He is quite gentle actually, don’t be scared! Cthulhu, illustration de Sofyan Syarief, Wikipedia I can help you, my friend