Improving the Flexibility and Robustness of Model-based Derivative-free Optimization Solvers

DOI10.1145/3338517zbMath1486.65064arXiv1804.00154OpenAlexW2968397096WikidataQ113309988 ScholiaQ113309988MaRDI QIDQ4960952

Jan Fiala, Coralia Cartis, Benjamin Marteau, Lindon Roberts

Publication date: 24 April 2020

Published in: ACM Transactions on Mathematical Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.00154

zbMATH Keywords

stochastic optimizationperformance evaluationleast-squaresmathematical softwarederivative-free optimizationtrust region methods

Mathematics Subject Classification ID

Numerical optimization and variational techniques (65K10)

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Uses Software

• BOBYQA
• NOWPAC
• CUTEst
• Py-BOBYQA

Cites Work

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• Variable-number sample-path optimization
• On trust region methods for unconstrained minimization without derivatives
• Best practices for comparing optimization algorithms
• Stochastic optimization using a trust-region method and random models
• Methods to compare expensive stochastic optimization algorithms with random restarts
• Least Frobenius norm updating of quadratic models that satisfy interpolation conditions
• A derivative-free trust-region algorithm for composite nonsmooth optimization
• Operational zones for comparing metaheuristic and deterministic one-dimensional global optimization algorithms
• A derivative-free Gauss-Newton method
• CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
• The calculus of simplex gradients
• Non-intrusive termination of noisy optimization
• Self-Correcting Geometry in Model-Based Algorithms for Derivative-Free Unconstrained Optimization
• A Derivative-Free Algorithm for Least-Squares Minimization
• Direct Multisearch for Multiobjective Optimization
• Geometry of sample sets in derivative-free optimization: polynomial regression and underdetermined interpolation
• Introduction to Derivative-Free Optimization
• Testing Unconstrained Optimization Software
• Trust Region Methods
• ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization
• BFO, A Trainable Derivative-free Brute Force Optimizer for Nonlinear Bound-constrained Optimization and Equilibrium Computations with Continuous and Discrete Variables
• Derivative-Free and Blackbox Optimization
• Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition
• Improving the Flexibility and Robustness of Model-based Derivative-free Optimization Solvers
• Benchmarking Derivative-Free Optimization Algorithms
• Derivative-Free Optimization of Expensive Functions with Computational Error Using Weighted Regression
• Benchmarking optimization software with performance profiles.