SNOBFIT (Stable Noisy Optimization by Branch and FIT) is a MATLAB 6 package for the robust and fast solution of noisy optimization problems with continuous variables varying within bound, possibly subject to additional soft constraints. Discrete variables are not supported. Objective function values must be provided by a file-based interface; care is taken that the optimization proceeds reasonably even when the interface produces noisy or even occasionally undefined results (hidden constraints). The interface makes it possible to use SNOBFIT with new data entered by hand, or by any automatic or semiautomatic experimental system. This makes SNOBFIT very suitable for applications to the selection of continuous parameter settings for simulations or experiments, performed with the goal of optimizing some user-specified criterion. Since multiple data points can be entered, SNOBFIT can take advantage of parallel function evaluations. The method combines a branching strategy to enhance the chance of finding a global minimum with a sequential quadratic programming method based on fitted quadratic models to have good local properties. Various safeguards address many possible pitfalls that may arise in practical applications, for which most other optimization routines are ill-prepared. Soft constraints are taken care of by a new penalty-type method with strong theoretical properties. In order to use SNOBFIT, one needs the MINQ bound-constrained quadratic programming package. (Source:

References in zbMATH (referenced in 25 articles )

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  1. Kimiaei, Morteza; Neumaier, Arnold: Efficient unconstrained black box optimization (2022)
  2. Ploskas, Nikolaos; Sahinidis, Nikolaos V.: Review and comparison of algorithms and software for mixed-integer derivative-free optimization (2022)
  3. Zhai, Jianyuan; Boukouvala, Fani: Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization (2022)
  4. Alarie, Stéphane; Audet, Charles; Bouchet, Pierre-Yves; Digabel, Sébastien Le: Optimization of stochastic blackboxes with adaptive precision (2021)
  5. Ma, Kaiwen; Sahinidis, Nikolaos V.; Rajagopalan, Sreekanth; Amaran, Satyajith; Bury, Scott J.: Decomposition in derivative-free optimization (2021)
  6. Bajaj, Ishan; Faruque Hasan, M. M.: Deterministic global derivative-free optimization of black-box problems with bounded Hessian (2020)
  7. Sauk, Benjamin; Ploskas, Nikolaos; Sahinidis, Nikolaos: GPU parameter tuning for tall and skinny dense linear least squares problems (2020)
  8. Larson, Jeffrey; Menickelly, Matt; Wild, Stefan M.: Derivative-free optimization methods (2019)
  9. Müller, Juliane; Day, Marcus: Surrogate optimization of computationally expensive black-box problems with hidden constraints (2019)
  10. Audet, Charles; Ihaddadene, Amina; Le Digabel, Sébastien; Tribes, Christophe: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm (2018)
  11. Costa, Alberto; Nannicini, Giacomo: RBFOpt: an open-source library for black-box optimization with costly function evaluations (2018)
  12. Huyer, Waltraud; Neumaier, Arnold: MINQ8: general definite and bound constrained indefinite quadratic programming (2018)
  13. Schwarz, Hannes; Bertsch, Valentin; Fichtner, Wolf: Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter (2018)
  14. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2016)
  15. Boukouvala, Fani; Misener, Ruth; Floudas, Christodoulos A.: Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO (2016)
  16. Lazar, Markus; Jarre, Florian: Calibration by optimization without using derivatives (2016)
  17. Custódio, A. L.; Madeira, J. F. A.: GLODS: global and local optimization using direct search (2015)
  18. Amaran, Satyajith; Sahinidis, Nikolaos V.; Sharda, Bikram; Bury, Scott J.: Simulation optimization: a review of algorithms and applications (2014)
  19. Billups, Stephen C.; Larson, Jeffrey; Graf, Peter: Derivative-free optimization of expensive functions with computational error using weighted regression (2013)
  20. Fowkes, Jaroslav M.; Gould, Nicholas I. M.; Farmer, Chris L.: A branch and bound algorithm for the global optimization of Hessian Lipschitz continuous functions (2013)

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