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limbo [![Build Status](https://travis-ci.org/resibots/limbo.svg?branch=master)](https://travis-ci.org/resibots/limbo)
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A lightweight framework for Bayesian and model-based optimization of black-box functions (C++11).
A lightweight framework for Bayesian optimization of black-box functions (C++11) and, more generally, for data-efficient optimization. It is designed to be very fast and very flexible.
Documentation
-------------
Documentation is available here: http://www.resibots.eu/limbo
Authors
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- Antoine Cully (Imperial College): http://www.isir.upmc.fr/?op=view_profil&lang=fr&id=278
- Jean-Baptiste Mouret (Inria): http://pages.isir.upmc.fr/~mouret/website/
- Antoine Cully (Imperial College): http://www.antoinecully.com
- Jean-Baptiste Mouret (Inria): http://members.loria.fr/JBMouret
- Konstantinos Chatzilygeroudis (Inria)
- Federico Allocati (Inria)
Limbo is partly funded by the ResiBots ERC Project (http://www.resibots.eu).
Main features
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- Bayesian optimization based on Gaussian processes
- Generic framework (template-based), which allows easy customization for testing original ideas
- Can exploit multicore computers
- Experimental support for some multi-objective algorithms
Main references
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- **General introduction:** Brochu, E., Cora, V. M., & De Freitas, N. (2010). A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. *arXiv preprint arXiv:1012.2599*.
- **Gaussian Processes (GP)**: Rasmussen, C. A, Williams C. K. I. (2006). /Gaussian Processes for Machine Learning./ MIT Press.
- **Optimizing hyperparameters:** Blum, M., & Riedmiller, M. (2013). Optimization of Gaussian Process Hyperparameters using Rprop. In *European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning*.
- **Parego (Multi-objective optimization):** Knowles, J. (2006). ParEGO: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. *Evolutionary Computation, IEEE Transactions on*, 10(1), 50-66.
- **CMA-ES (inner optimization):** Auger, A., & Hansen, N. (2005). A restart CMA evolution strategy with increasing population size. In *Evolutionary Computation, 2005. The 2005 IEEE Congress on* (Vol. 2, pp. 1769-1776). IEEE.
- **Expected hypervolume improvement (multi-objective optimization):** Hupkens, I., Emmerich, M. T. M., Deutz A. H. (2014). Faster Computation of Expected Hypervolume Improvement. arXiv: http://arxiv.org/abs/1408.7114
Other libraries
---------------
Limbo is a framework for our research that is voluntarily kept small. It is designed to be very fast and flexible, but it does not aim at covering every possible use case for Bayesian optimization.
If you need a more full-featured library, check:
- BayesOpt: http://rmcantin.bitbucket.org/html/
- libGP (no optimization): https://github.com/mblum/libgp
- Implementation of the classic algorithms (Bayesian optimization, many kernels, likelihood maximization, etc.)
- Modern C++-11
- Generic framework (template-based / policy-based design), which allows for easy customization, to test novel ideas
- Experimental framework that allows user to easily test variants of experiments, compare treatments, submit jobs to clusters (OAR scheduler), etc.
- High performance (in particular, Limbo can exploit multicore computers via Intel TBB and vectorize some operations via Eigen3)
- Purposely small to be easily maintained and quickly understood
Scientific articles that use Limbo
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Cully, A., Clune, J., Tarapore, D., & Mouret, J. B. (2015). Robots that can adapt like animals. *Nature*, 521(7553), 503-507.
Research project that use Limbo
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- Resibots. ERC Starting Grant: http://www.resibots.eu/
- PAL. H2020 EU project: http://www.pal4u.eu/
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