A generalized super-memory gradient projection method of strongly sub-feasible directions with strong convergence for nonlinear inequality constrained optimization

DOI10.1016/j.camwa.2007.01.016zbMath1126.90424OpenAlexW2105460687MaRDI QIDQ2460608

You-fang Zeng, Jin-Bao Jian, Chun-Ming Tang

Publication date: 12 November 2007

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.camwa.2007.01.016

zbMATH Keywords

global convergencestrong convergenceInequality constrained optimizationstrongly sub-feasible directionssuper-memory gradient method

Mathematics Subject Classification ID

Nonlinear programming (90C30) Methods of reduced gradient type (90C52)

Related Items (4)

Quadratically constraint quadratical algorithm model for nonlinear minimax problems ⋮ A novel fractional Tikhonov regularization coupled with an improved super-memory gradient method and application to dynamic force identification problems ⋮ A strongly sub-feasible primal-dual quasi interior-point algorithm for nonlinear inequality constrained optimization ⋮ A novel method of dynamic force identification and its application

Cites Work

• Test examples for nonlinear programming codes
• Supermemory descent methods for unconstrained minimization
• Study on a supermemory gradient method for the minimization of functions
• A new super-memory gradient method with curve search rule
• A new norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization
• A New QP-Free, Globally Convergent, Locally Superlinearly Convergent Algorithm For Inequality Constrained Optimization
• The Gradient Projection Method for Nonlinear Programming. Part I. Linear Constraints
• The Gradient Projection Method for Nonlinear Programming. Part II. Nonlinear Constraints
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