An adaptive conic trust-region method for unconstrained optimization

DOI10.1080/10556780410001697677zbMath1127.90415OpenAlexW1987960975MaRDI QIDQ3369518

Raimundo J. B. de Sampaio, Qiaoming Han, Ji-ye Han, Wen-Yu Sun

Publication date: 2 February 2006

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556780410001697677

zbMATH Keywords

nonlinear programmingquasi-Newton methodconic modeltrust-region methodself-adjust strategy

Mathematics Subject Classification ID

Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Methods of successive quadratic programming type (90C55)

Related Items (11)

A new subspace minimization conjugate gradient method based on tensor model for unconstrained optimization ⋮ A nonmonotone adaptive trust region method for unconstrained optimization based on conic model ⋮ Exact two steps SOCP/SDP formulation for a modified conic trust region subproblem ⋮ A modified trust region method with beale's PCG technique for optimization ⋮ Convergence of memory gradient methods ⋮ An efficient gradient method with approximate optimal stepsize for large-scale unconstrained optimization ⋮ A hybrid trust region algorithm for unconstrained optimization ⋮ Nonmonotone adaptive trust region method based on simple conic model for unconstrained optimization ⋮ An iterative algorithm for the conic trust region subproblem ⋮ A CONIC AFFINE SCALING DOGLEG METHOD FOR NONLINEAR OPTIMIZATION WITH BOUND CONSTRAINTS ⋮ An adaptive approach of conic trust-region method for unconstrained optimization problems

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Global convergence of nonmonotone descent methods for unconstrained optimization problems
• Some investigations in a new algorithm for nonlinear optimization based on conic models of the objective function
• Interpolation by conic model for unconstrained optimization
• Deriving collinear scaling algorithms as extensions of quasi-Newton methods and the local convergence of DFP- and BFGS-related collinear scaling algorithms
• Computing a Trust Region Step
• A Family of Trust-Region-Based Algorithms for Unconstrained Minimization with Strong Global Convergence Properties
• Conic Approximations and Collinear Scalings for Optimizers
• The Q-Superlinear Convergence of a Collinear Scaling Algorithm for Unconstrained Optimization
• Computing Optimal Locally Constrained Steps
• Newton’s Method with a Model Trust Region Modification
• Tensor Methods for Unconstrained Optimization Using Second Derivatives
• Local andQ-superlinear convergence of a class of collinear scaling algorithms that extends quasi-newton methods with broyden's bounded-⊘ class of updates† ‡
• Tensor Methods for Large, Sparse Unconstrained Optimization
• A conic trust-region method for nonlinearly constrained optimization

This page was built for publication: An adaptive conic trust-region method for unconstrained optimization