A new \(\varepsilon \)-generalized projection method of strongly sub-feasible directions for inequality constrained optimization

DOI10.1007/s11424-011-8105-5zbMath1254.90230OpenAlexW2020125359MaRDI QIDQ646746

Guodong Ma, Chuan-Hao Guo, Jin-Bao Jian

Publication date: 17 November 2011

Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11424-011-8105-5

zbMATH Keywords

optimizationinequality constraintsglobal and strong convergencemethod of strongly sub-feasible directions\(\varepsilon \)-generalized projection

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Related Items (2)

An \(\varepsilon\)-generalized gradient projection method for nonlinear minimax problems ⋮ A QP-free algorithm of quasi-strongly sub-feasible directions for inequality constrained optimization

Uses Software

• CUTEr
• CUTE

Cites Work

• Finitely convergent \(\varepsilon\)-generalized projection algorithm for nonlinear systems
• A new finitely convergent algorithm for systems of nonlinear inequalities
• Test examples for nonlinear programming codes
• The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints
• A finitely convergent algorithm for convex inequalities
• Combined phase I—phase II methods of feasible directions
• CUTE
• Rate of Convergence of a Class of Methods of Feasible Directions
• Methods for finding feasible points in constrained optimization
• On the Convergence of Some Feasible Direction Algorithms for Nonlinear Programming
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