Results 11 to 20 of about 66,063 (262)
ZOOpt: a toolbox for derivative-free optimization
Science China Information Sciences, 2022 SCIENCE CHINA Information Sciences, 2022.
Yu-Ren Liu +4 more
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Inexact Derivative-Free Optimization for Bilevel Learning [PDF]
Journal of Mathematical Imaging and Vision, 2021 AbstractVariational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user. A by-now common strategy to resolve this issue is to learn these parameters from data.
Matthias J. Ehrhardt, Lindon Roberts
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Derivative-free optimization methods [PDF]
Acta Numerica, 2019 In many optimization problems arising from scientific, engineering and artificial intelligence applications, objective and constraint functions are available only as the output of a black-box or simulation oracle that does not provide derivative information. Such settings necessitate the use of methods for derivative-free, or zeroth-order, optimization.
Jeffrey Larson 0001 +2 more
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A Derivative-Free Method for Structured Optimization Problems [PDF]
SIAM Journal on Optimization, 2021 Structured optimization problems are ubiquitous in fields like data science and engineering. The goal in structured optimization is using a prescribed set of points, called atoms, to build up a solution that minimizes or maximizes a given function. In the present paper, we want to minimize a black-box function over the convex hull of a given set of ...
Cristofari A., Rinaldi F.
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Mathematics, 2022 This article considers a box-constrained global optimization problem for Lipschitz continuous functions with an unknown Lipschitz constant. The well-known derivative-free global search algorithm DIRECT (DIvide RECTangle) is a promising approach for such ...
Linas Stripinis, Remigijus Paulavičius
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Derivative-free optimization for optical chirality enhancement [PDF]
EPJ Web of ConferencesWe adopt a multi-objective optimization approach to design one-dimensional photonic crystals with large optical chirality enhancements. We show that this technique allows for a large design flexibility in terms of selected materials and operational ...
Pellegrini Giovanni +5 more
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Journal of Inequalities and Applications, 2018 This paper focuses on a class of nonlinear optimization subject to linear inequality constraints with unavailable-derivative objective functions. We propose a derivative-free trust-region methods with interior backtracking technique for this optimization.
Jing Gao, Jian Cao
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Derivative-Free Optimal Iterative Methods
Computational Methods in Applied Mathematics, 2010 AbstractIn this study, we develop an optimal family of derivative-free iterative methods. Convergence analysis shows that the methods are fourth order convergent, which is also verified numerically. The methods require three functional evaluations during each iteration. Though the methods are independent of derivatives, computa-
Sanjay Kumar Khattri, Ravi P. Agarwal
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Mathematical and Computational Applications, 2021 We present a local trust region descent algorithm for unconstrained and convexly constrained multiobjective optimization problems. It is targeted at heterogeneous and expensive problems, i.e., problems that have at least one objective function that is ...
Manuel Berkemeier, Sebastian Peitz
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Mathematics, 2014 A shape optimization method is used to study the exterior Bernoulli free boundaryproblem. We minimize the Kohn–Vogelius-type cost functional over a class of admissibledomains subject to two boundary value problems. The first-order shape derivative of the
Jerico B. Bacani, Gunther Peichl
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