Projected adaptive cubic regularization algorithm with derivative-free filter technique for box constrained optimization

DOI10.1155/2021/1496048zbMath1486.90187OpenAlexW3209962261MaRDI QIDQ2244360

Peng Wang, Lingyun He, De-Tong Zhu

Publication date: 12 November 2021

Published in: Discrete Dynamics in Nature and Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2021/1496048

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)

Uses Software

• NEWUOA
• BOBYQA

Cites Work

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• Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
• Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
• Updating the regularization parameter in the adaptive cubic regularization algorithm
• Test examples for nonlinear programming codes
• An affine scaling interior trust-region method combining with line search filter technique for optimization subject to bounds on variables
• A derivative-free method for solving box-constrained underdetermined nonlinear systems of equations
• An interior affine scaling cubic regularization algorithm for derivative-free optimization subject to bound constraints
• An affine scaling trust-region algorithm with interior backtracking technique for solving bound-constrained nonlinear systems
• Nonmonotone Trust-Region Methods for Bound-Constrained Semismooth Equations with Applications to Nonlinear Mixed Complementarity Problems
• On sequential and parallel non-monotone derivative-free algorithms for box constrained optimization
• On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization
• Convergence of Trust-Region Methods Based on Probabilistic Models
• An active-set trust-region method for derivative-free nonlinear bound-constrained optimization
• Developments of NEWUOA for minimization without derivatives
• On the geometry phase in model-based algorithms for derivative-free optimization
• Introduction to Derivative-Free Optimization
• Globally Convergent Inexact Newton Methods
• An adaptive cubic regularization algorithm for nonconvex optimization with convex constraints and its function-evaluation complexity
• An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
• A derivative-free affine scaling trust region methods based on probabilistic models with new nonmonotone line search technique for linear inequality constrained minimization without strict complementarity
• Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points

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