Quasi-Newton trust region algorithm for non-smooth least squares problems

DOI10.1016/S0096-3003(98)10103-0zbMath1026.90066OpenAlexW1998948152MaRDI QIDQ1569085

N. Delaunay

Publication date: 25 June 2000

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

Full work available at URL: https://doi.org/10.1016/s0096-3003(98)10103-0

zbMATH Keywords

convergence ratenon-smooth optimizationlocally Lipschitzian functionquasi-Newton trust region algorithm

Mathematics Subject Classification ID

Quadratic programming (90C20) Methods of quasi-Newton type (90C53)

Related Items (7)

Nonmonotone trust region method for solving optimization problems ⋮ A superlinearly convergent ODE-type trust region algorithm for nonsmooth nonlinear equations ⋮ Accelerating the convergence in the single-source and multi-source Weber problems ⋮ An adaptive trust region algorithm for large-residual nonsmooth least squares problems ⋮ Nonmonotone adaptive trust-region method for unconstrained optimization problems ⋮ On image restoration from random sampling noisy frequency data with regularization ⋮ An adaptive approach of conic trust-region method for unconstrained optimization problems

Cites Work

• On the convergence of some quasi-Newton methods for nonlinear equations with nondifferentiable operators
• Newton's method and quasi-Newton-SQP method for general \(\text{LC}^1\) constrained optimization
• Trust region algorithm for nonsmooth optimization
• A nonsmooth version of Newton's method
• Conditions for convergence of trust region algorithms for nonsmooth optimization
• The majorant method in the theory of newton-kantorovich approximations and the pták error estimates
• Newton’s Method with a Model Trust Region Modification
• Globally Convergent Newton Methods for Nonsmooth Equations
• Least change update methods for nonlinear systems with nondifferentiable terms^∗
• Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations
• Trust Region Algorithms for Solving Nonsmooth Equations
• An algorithm for solving linearly constrained optimization problems
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