Journal Article10.1007/s10589-022-00373-z
Quadratic regularization methods with finite-difference gradient approximations
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TL;DR: This paper presents two quadratic regularization methods with finite-difference gradient approximations for smooth unconstrained optimization problems and proves the relative efficiency of the proposed methods.
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About: This article is published in Computational Optimization and Applications. The article was published on 18 May 2022. The article focuses on the topics: Computer science & Hessian matrix.
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Testing Unconstrained Optimization Software
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Random Gradient-Free Minimization of Convex Functions
Yurii Nesterov,Vladimir Spokoiny +1 more
TL;DR: New complexity bounds for methods of convex optimization based only on computation of the function value are proved, which appears that such methods usually need at most n times more iterations than the standard gradient methods, where n is the dimension of the space of variables.
Benchmarking Derivative-Free Optimization Algorithms
Jorge J. Moré,Stefan M. Wild +1 more
TL;DR: This work uses performance and data profiles, together with a convergence test that measures the decrease in function value, to analyze the performance of three solvers on sets of smooth, noisy, and piecewise-smooth problems.
Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results
TL;DR: An Adaptive Regularisation algorithm using Cubics (ARC) is proposed for unconstrained optimization, generalizing at the same time an unpublished method due to Griewank, an algorithm by Nesterov and Polyak and a proposal by Weiser et al.