Benchmarking Derivative-Free Optimization Algorithms

By Jorge J. Moré, Stefan M. Wild. SIAM Journal on Optimization, 2009

700 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/90319b0b2a3c700015303fa2f30dcdc22f081ac1

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Citations in the corpus

Performance indicators in multiobjective optimization (2018, methodology): “In single-objective optimization, the most used graphical tools to compare algorithms include performance profiles [66] and data profiles [67] (see also [68] for a detailed survey on the tools to compare single-optimization algorithms).”
Derivative-free optimization: a review of algorithms and comparison of software implementations (2012, background): “Other comparisons, such as [11,33,38,48,53,65,67,97,138], are restricted to a few solvers related to the algorithms proposed in these papers.”
Sustainability SI: Optimal Prices of Electricity at Public Charging Stations for Plug-in Electric Vehicles (2016, methodology): “…algorithm, a pattern search method guided by simplex derivatives, to solve the bi-level model (Custódio and Vicente 2007; Custódio et al. 2010), after reviewing and comparing a variety of derivative-free algorithms including metaheuristics (e.g., Yin 2000; Kolda et al. 2003; More and Wild 2009).”
COCO: a platform for comparing continuous optimizers in a black-box setting (2016, background): “The latter compute the target value for each problem individually as fbest + τ(f0 − fbest), based on the best achieved f -value from a set of solvers within a given budget and for the precision parameter τ [55].”
Direct Multisearch for Multiobjective Optimization (2011, background): “Following [37], we also divided σ in (5.”
Smoothing and worst-case complexity for direct-search methods in nonsmooth optimization (2013, methodology): “We have made a number of experiments regarding the use of smoothing in direct search, on a test set suggested in [28] consisting of 53 problems of the form minx∈Rn f(x) = ‖F (x)‖1, where F varies among 22 nonlinear vector functions of the CUTEr collection [19] with 2 ≤ n ≤ 12 and different initial p”
Line search methods with variable sample size for unconstrained optimization (2013, background): “Key words: stochastic optimization, line search, simulations, sample average approximation, variable sample size”
Simulation optimization: a review of algorithms and applications (2014, methodology): “For derivative-free algorithms, data proﬁles are commonly used [141], where the fraction of problems solved after a certain number of iterations (function evaluations) or ‘simplex gradients’ is shown.”