Variable metric gradient projection processes in convex feasible sets defined by nonlinear inequalities

DOI10.1007/BF01448362zbMath0642.90081OpenAlexW2054435254MaRDI QIDQ1101344

B. George

Publication date: 1988

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

Full work available at URL: https://doi.org/10.1007/bf01448362

zbMATH Keywords

Hilbert spacegradient projection methodslinear rate of convergencefinitely many inequality constraintsnonsingularity of minimal pointssecond order sufficient optimality conditions

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Programming in abstract spaces (90C48) Methods of reduced gradient type (90C52)

Related Items

A projected Newton method in a Cartesian product of balls, A projected Newton method for minimization problems with nonlinear inequality constraints, An Extension of the Projected Gradient Method to a Banach Space Setting with Application in Structural Topology Optimization, Error bounds and convergence analysis of feasible descent methods: A general approach

Cites Work

• A class of superlinearly convergent projection algorithms with relaxed stepsizes
• On the convergence of projected gradient processes to singular critical points
• Newton-Goldstein convergence rates for convex constrained minimization problems with singular solutions
• Two-Metric Projection Methods for Constrained Optimization
• Newton’s Method and the Goldstein Step-Length Rule for Constrained Minimization Problems
• On the Goldstein-Levitin-Polyak gradient projection method
• Projected Newton Methods for Optimization Problems with Simple Constraints
• Convex programming in Hilbert space
• On Steepest Descent