A quasi-Newton trust region method with a new conic model for the unconstrained optimization

DOI10.1016/j.amc.2008.06.062zbMath1167.65035OpenAlexW2022359937MaRDI QIDQ2378932

Qin Ni, Xiaoping Lu

Publication date: 14 January 2009

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2008.06.062

zbMATH Keywords

algorithmunconstrained optimizationconvergencenumerical resultstrust region methodquasi-Newton methodconic modeldogleg step

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Interior-point methods (90C51)

Related Items (10)

A new adaptive trust region algorithm for optimization problems ⋮ Exact two steps SOCP/SDP formulation for a modified conic trust region subproblem ⋮ A nonmonotone adaptive trust region method based on conic model for unconstrained optimization ⋮ A simple alternating direction method for the conic trust region subproblem ⋮ A fractional trust region method for linear equality constrained optimization ⋮ A quasi-Newton trust region method based on a new fractional model ⋮ Nonlinear stepsize control algorithms: complexity bounds for first- and second-order optimality ⋮ An iterative algorithm for the conic trust region subproblem ⋮ On the worst-case complexity of nonlinear stepsize control algorithms for convex unconstrained optimization ⋮ A new alternating direction trust region method based on conic model for solving unconstrained optimization

Uses Software

• minpack

Cites Work

• Deriving collinear scaling algorithms as extensions of quasi-Newton methods and the local convergence of DFP- and BFGS-related collinear scaling algorithms
• Conic Approximations and Collinear Scalings for Optimizers
• The Q-Superlinear Convergence of a Collinear Scaling Algorithm for Unconstrained Optimization
• Testing Unconstrained Optimization Software
• Local andQ-superlinear convergence of a class of collinear scaling algorithms that extends quasi-newton methods with broyden's bounded-⊘ class of updates† ‡
• A Conic Algorithm for Optimization
• Optimality Conditions for Trust-Region Subproblems Involving a Conic Model
• Benchmarking optimization software with performance profiles.
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