This Fortran software seeks the least value of a function of several variables without requiring any derivatives of the objective function. It was developed from my NEWUOA package for this calculation in the unconstrained case. The main new feature of BOBYQA, however, is that it allows lower and upper bounds on each variable. The name BOBYQA denotes Bound Optimization BY Quadratic Approximation. Please send an e-mail to me at if you would like to receive a free copy of the Fortran software. As far as I know BOBYQA is the most powerful package available at present for minimizing functions of hundreds of variables without derivatives subject to simple bound constraints. There are no restrictions on its use. I would be delighted if it becomes valuable to much research and many applications

References in zbMATH (referenced in 26 articles )

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  1. Boukouvala, Fani; Faruque Hasan, M.M.; Floudas, Christodoulos A.: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption (2017)
  2. Arouxét, Ma.Belén; Echebest, Nélida E.; Pilotta, Elvio A.: Inexact restoration method for nonlinear optimization without derivatives (2015)
  3. Conejo, P.D.; Karas, E.W.; Pedroso, L.G.: A trust-region derivative-free algorithm for constrained optimization (2015)
  4. Ferreira, Priscila S.; Karas, Elizabeth W.; Sachine, Mael: A globally convergent trust-region algorithm for unconstrained derivative-free optimization (2015)
  5. Gould, Nicholas I.M.; Orban, Dominique; Toint, Philippe L.: CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization (2015)
  6. Newby, Eric; Ali, M.M.: A trust-region-based derivative free algorithm for mixed integer programming (2015)
  7. Sampaio, Ph.R.; Toint, Ph.L.: A derivative-free trust-funnel method for equality-constrained nonlinear optimization (2015)
  8. Gao, Jing; Zhu, Detong: An affine scaling derivative-free trust region method with interior backtracking technique for bounded-constrained nonlinear programming (2014)
  9. Gumma, E.A.E.; Hashim, M.H.A.; Ali, M.Montaz: A derivative-free algorithm for linearly constrained optimization problems (2014)
  10. Lara, Pedro C.S.; Portugal, Renato; Lavor, Carlile: A new hybrid classical-quantum algorithm for continuous global optimization problems (2014)
  11. Zhang, Zaikun: Sobolev seminorm of quadratic functions with applications to derivative-free optimization (2014)
  12. Červinka, M.; Matonoha, C.; Outrata, J.V.: On the computation of relaxed pessimistic solutions to MPECs (2013)
  13. Conejo, P.D.; Karas, E.W.; Pedroso, L.G.; Ribeiro, A.A.; Sachine, M.: Global convergence of trust-region algorithms for convex constrained minimization without derivatives (2013)
  14. Martínez, J.M.; Sobral, F.N.C.: Constrained derivative-free optimization on thin domains (2013)
  15. Murray, Kevin; Müller, Samuel; Turlach, Berwin A.: Revisiting fitting monotone polynomials to data (2013)
  16. Powell, M.J.D.: Beyond symmetric Broyden for updating quadratic models in minimization without derivatives (2013)
  17. Rios, Luis Miguel; Sahinidis, Nikolaos V.: Derivative-free optimization: a review of algorithms and comparison of software implementations (2013)
  18. Zhao, Hui; Li, Gaoming; Reynolds, Albert C.; Yao, Jun: Large-scale history matching with quadratic interpolation models (2013)
  19. Albrecht, Sebastian; Leibold, Marion; Ulbrich, Michael: A bilevel optimization approach to obtain optimal cost functions for human arm movements (2012)
  20. Dawson, John A.; Kendziorski, Christina: An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments (2012)

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