BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits. BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization characterized for being sample efficient as it builds a posterior distribution to capture the evidence and prior knowledge of the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Karban, Pavel; Pánek, David; Orosz, Tamás; Petrášová, Iveta; Doležel, Ivo: FEM based robust design optimization with Agros and Ārtap (2021)
- Merrill, Erich; Fern, Alan; Fern, Xiaoli; Dolatnia, Nima: An empirical study of Bayesian optimization: acquisition versus partition (2021)
- Antoine Cully; Konstantinos Chatzilygeroudis; Federico Allocati; Jean-Baptiste Mouret: Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization (2018) not zbMATH
- Karban, Pavel; Kropík, Petr; Kotlan, Václav; Doležel, Ivo: Bayes approach to solving T.E.A.M. benchmark problems 22 and 25 and its comparison with other optimization techniques (2018)
- Mai, Feng; Fry, Michael J.; Ohlmann, Jeffrey W.: Model-based capacitated clustering with posterior regularization (2018)
- Law, T.; Shawe-Taylor, J.: Practical Bayesian support vector regression for financial time series prediction and market condition change detection (2017)
- Martinez-Cantin, Ruben: BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014)
Further publications can be found at: http://rmcantin.bitbucket.org/html/citelist.html