The BOBYQA algorithm for bound constrained optimization without derivatives

By M. Powell. 2009

1,544 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0d2edc46f81f9a0b0b62937507ad977b46729f64

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Citations in the corpus

Joint optimization of oil well placement and controls (2012, methodology): “Examples of these techniques are Hooke–Jeeves direct search (HJDS; [25]), generalized pattern search (GPS; [36], [2]), mesh adaptive direct search (MADS; [3]), and bound optimization by quadratic approximation (BOBYQA; [33]).”
CUTEst: a Constrained and Unconstrained Testing Environment with safe threads for mathematical optimization (2013, unknown): “In addition to the still-current packages supported by CUTEr, CUTEst provides new interfaces to ALGENCAN [1], BOBYQA [19], Direct Search [7], filterSD [8], NEWUOA [18], NLPQLP [21], NOMAD [15], PENNLP [14], QL [20], SPG [2] and SQIC [10], as well as various new packages within GALAHAD [12].”
Predicting microbial nitrate reduction pathways in coastal sediments (2014, methodology): “Z Phys Chem 216: 51−63 Powell M (2010) The BOBYQA algorithm for bound constrained optimization without derivatives.”
Female Labour Supply, Human Capital and Welfare Reform (2013, methodology): “We use the Bound Optimization By Quadratic Approximation implemented by the Numerical Algorithms Group (Nag, see Powell, 2009). See Altonji and Segal (1996) on the small sample issue of weighted minimum distance estimators.”
Fitting Linear Mixed-Effects Models Using lme4 (2014, methodology): “lme4 incorporates two built-in optimization choices, an implementation of the Nelder-Mead simplex algorithm adapted from Steven Johnson’s NLopt C library (Johnson 2014) and a wrapper for Powell’s BOBYQA algorithm, implemented in the minqa package Bates, Mullen, Nash, and Varadhan (2014) as a wrapper”
BayesOpt: a Bayesian optimization library for nonlinear optimization, experimental design and bandits (2014, methodology): “…Bayes (ML or MAP of θ ), we found that a combination of global and local derivative free methods such as DIRECT (Jones et al., 1993) and BOBYQA (Powell, 2009) marginally outperforms in CPU time to gradient based method for optimizing θ by avoiding the overhead of computing the marginal…”
Imaging brain microstructure with diffusion MRI: practicality and applications (2019, methodology): “algorithm [227] to exploit spatial coherence during fitting of the DIAMOND model.”
A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls (2014, methodology): “Existing approaches include the use of kriging [30], quadratic approximations [23, 38], and reducedorder numerical models [11].”
From human to humanoid locomotion—an inverse optimal control approach (2010, methodology): “We use the newly released derivative-free optimization code BOBYQA by Powell (2008, 2009).”
Variational Quantum Linear Solver (2019, methodology): “, the COBYLA algorithm [47]), as well as gradient-based optimization methods (such as the L-BFGS-B algorithm [48]).”
Early dark energy does not restore cosmological concordance (2020, methodology): “We sample from the posterior distributions using the Metropolis-Hastings algorithm implemented in Cobaya (Lewis & Bridle (2002); Lewis (2013); Neal (2005)), with a Gelman-Rubin (Gelman & Rubin (1992)) convergence criterion R − 1 < 0.05 (unless otherwise stated).”
Cobaya: code for Bayesian analysis of hierarchical physical models (2020, methodology): “In addition to Monte Carlo samples, Cobaya includes a importance-weighting tool, and interfaces with popular minimizers for posterior and likelihood maximization (from scipy [16] and Py-BOBYQA implementation [17] of the BOBYQA algorithm [18]) Cobaya is also ready for highperformance computation in c”
Advanced line sampling for efficient robust reliability analysis (2015, unknown): “[29], as global and local searchers, respectively.”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “Preliminary model calibration using the BOBYQA algorithm (Powell, 2009) and the default setting gave reasonable model performance.”
A Survey of Automatic Parameter Tuning Methods for Metaheuristics (2020, methodology): “In that work, bound optimization by quadratic approximation (BOBYQA) [61] was combined with repeated evaluation, and mesh adaptive direct search (MADS) [62] was combined with both repeated evaluation and F -Race to solve parameter tuning problems.”
Derivative-free optimization: a review of algorithms and comparison of software implementations (2012, methodology): “BOBYQA is an extension of the NEWUOA algorithm to bounded problems with additional considerations on the set of rules that maintain the set of interpolating points. is a genetic algorithm implemented in multiple languages including C, Matlab, and Python.”