Globally and superlinearly convergent trust-region algorithm for convex \(SC^ 1\)-minimization problems and its application to stochastic programs

DOI10.1007/BF02189800zbMath0866.90093OpenAlexW1974546081MaRDI QIDQ2565034

Liqun Qi, Houyuan Jiang

Publication date: 20 July 1997

Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02189800

zbMATH Keywords

global convergencetrust-region algorithmsuperlinear convergencestochastic quadratic programmingSC\(^ 1\)-function

Mathematics Subject Classification ID

Convex programming (90C25) Stochastic programming (90C15)

Related Items (4)

Solution of monotone complementarity problems with locally Lipschitzian functions ⋮ Local feasible QP-free algorithms for the constrained minimization of SC\(^1\) functions ⋮ A globally and superlinearly convergent trust region method for \(LC^1\) optimization problems ⋮ The \(SC^1\) 1property of an expected residual function arising from stochastic complementarity problems

Cites Work

• Unnamed Item
• Convergence properties of trust region methods for linear and convex constraints
• Superlinearly convergent approximate Newton methods for LC\(^ 1\) optimization problems
• Computational schemes for large-scale problems in extended linear- quadratic programming
• An SQP algorithm for extended linear-quadratic problems in stochastic programming
• A globally convergent Newton method for convex \(SC^ 1\) minimization problems
• Newton's method for quadratic stochastic programs with recourse
• Local uniqueness and convergence of iterative methods for nonsmooth variational inequalities
• A nonsmooth version of Newton's method
• Conditions for convergence of trust region algorithms for nonsmooth optimization
• On the superlinear convergence of a trust region algorithm for nonsmooth optimization
• Optimization and nonsmooth analysis
• A Lagrangian finite generation technique for solving linear-quadratic problems in stochastic programming
• A model algorithm for composite nondifferentiable optimization problems
• Newton’s Method with a Model Trust Region Modification
• Superlinearly convergent quasi-newton algorithms for nonlinearly constrained optimization problems
• Primal-Dual Projected Gradient Algorithms for Extended Linear-Quadratic Programming
• Convergence Analysis of Some Algorithms for Solving Nonsmooth Equations
• Global Convergence of a Class of Trust Region Algorithms for Optimization Using Inexact Projections on Convex Constraints
• Linear-Quadratic Programming and Optimal Control

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