Superlinearly convergent affine scaling interior trust-region method for linear constrained \(LC^{1}\) minimization

DOI10.1007/s10114-008-5444-9zbMath1167.90021OpenAlexW2380321685MaRDI QIDQ960640

De-Tong Zhu

Publication date: 5 January 2009

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-008-5444-9

zbMATH Keywords

global convergencesuperlinear convergencebacktrackingtrust-region methodinterior pointlinearly constrained optimizationaffine scaling

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items (3)

On the global convergence of a projective trust region algorithm for nonlinear equality constrained optimization ⋮ Global and local convergence of a new affine scaling trust region algorithm for linearly constrained optimization ⋮ An Affine Scaling Interior Point Filter Line-Search Algorithm for Linear Inequality Constrained Minimization

Cites Work

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• A new affine scaling interior point algorithm for nonlinear optimization subject to linear equality and inequality constraints.
• A trust region and affine scaling interior point method for nonconvex minimization with linear inequality constraints
• A nonsmooth version of Newton's method
• Optimization and nonsmooth analysis
• Weakly Differentiable Functions
• Numerical Optimization
• A special newton-type optimization method
• A Nonmonotone Line Search Technique for Newton’s Method
• Globally convergent inexact generalized Newton method for first-order differentiable optimization problems

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