A hybrid epigraph directions method for nonsmooth and nonconvex constrained optimization via generalized augmented Lagrangian duality and a genetic algorithm

DOI10.1155/2018/8750740zbMath1427.90261OpenAlexW2804781939WikidataQ129776623 ScholiaQ129776623MaRDI QIDQ1721580

Wilhelm P. Freire, Tales L. Fonseca, Hernando J. R. Franco, Afonso C. C. Lemonge

Publication date: 8 February 2019

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/8750740

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)

Uses Software

• Matlab
• NSO

Cites Work

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• A feasible directions method for nonsmooth convex optimization
• Interior epigraph directions method for nonsmooth and nonconvex optimization via generalized augmented Lagrangian duality
• Evolutionary constrained optimization
• Globally convergent cutting plane method for nonconvex nonsmooth minimization
• Duality and exact penalization for general augmented Lagrangians
• Test example for nonlinear programming codes
• Feasible direction interior-point technique for nonlinear optimization
• Augmented Lagrangian duality and nondifferentiable optimization methods in nonconvex programming
• An inexact modified subgradient algorithm for primal-dual problems via augmented Lagrangians
• An inexact modified subgradient algorithm for nonconvex optimization
• A primal dual modified subgradient algorithm with sharp Lagrangian
• An update rule and a convergence result for a penalty function method
• On a modified subgradient algorithm for dual problems via sharp augmented Lagrangian
• A Deflected Subgradient Method Using a General Augmented Lagrangian Duality with Implications on Penalty Methods
• The extended/generalized finite element method: An overview of the method and its applications
• An adaptive penalty scheme for genetic algorithms in structural optimization