Results 31 to 40 of about 616,628 (303)
A constrained multi-objective surrogate-based optimization algorithm [PDF]
, 2014 Surrogate models or metamodels are widely used in the realm of engineering for design optimization to minimize the number of computationally expensive simulations.
Couckuyt, Ivo +3 more
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Atti della Accademia Peloritana dei Pericolanti : Classe di Scienze Fisiche, Matematiche e Naturali, 2020 This is a companion paper to "Ghost penalties in nonconvex constrained optimization: Diminishing stepsizes and iteration complexity" (to appear in Mathematics of Operations Research).
Francisco Facchinei +3 more
doaj +1 more source
A Bayesian approach to constrained single- and multi-objective optimization [PDF]
, 2016 This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate.
A Bayesian +7 more
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Spectrally Constrained Optimization
Journal of Scientific ComputingWe investigate how to solve smooth matrix optimization problems with general linear inequality constraints on the eigenvalues of a symmetric matrix. We present solution methods to obtain exact global minima for linear objective functions, i.e., $F(X) = \langle C, X \rangle$, and perform exact projections onto the eigenvalue constraint set.
Casey Garner +2 more
openaire +2 more sources
, 2015 Different types of evolutionary algorithms have been developed for constrained continuous optimization. We carry out a feature-based analysis of evolved constrained continuous optimization instances to understand the characteristics of constraints that ...
Neumann, FranK, Poursoltan, Shayan
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Constrained Multiobjective Biogeography Optimization Algorithm
The Scientific World Journal, 2014 Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed.
Hongwei Mo +4 more
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Sensitivity-Based Change Detection for Dynamic Constrained Optimization
IEEE Access, 2020 In dynamic constrained optimization, changes may occur in either the objective function or constraint functions, or both. However, although research on dynamic optimization has been growing significantly, it is centered mainly around unconstrained ...
Noha Hamza, Ruhul Sarker, Daryl Essam
doaj +1 more source
Matrix Completion via Max-Norm Constrained Optimization [PDF]
, 2016 Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting.
Cai, T. Tony, Zhou, Wen-Xin
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MOOSE Optimization Module: Physics-constrained optimization
SoftwareXThe MOOSE Optimization Module integrates optimization capabilities within the MOOSE framework, enabling efficient and accurate physics-constrained optimization. This module leverages automatic differentiation to compute Jacobians and employs an automatic
Zachary M. Prince +4 more
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, 2018 In the article we present a general theory of augmented Lagrangian functions for cone constrained optimization problems that allows one to study almost all known augmented Lagrangians for cone constrained programs within a unified framework. We develop a
Dolgopolik, M. V.
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