Global convergence analysis of algorithms for finding feasible points in norm-relaxed MFD

DOI10.1023/A:1021778002066zbMath0915.90234OpenAlexW1517219963MaRDI QIDQ1280097

Michael M. Kostreva, X. B. Chen

Publication date: 17 May 1999

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

Full work available at URL: https://doi.org/10.1023/a:1021778002066

zbMATH Keywords

global convergencenonlinear programmingmethod of feasible directionsfeasible pointsdirection-finding subproblem

Mathematics Subject Classification ID

Nonlinear programming (90C30)

Related Items (10)

A superlinearly convergent norm-relaxed SQP method of strongly sub-feasible directions for constrained optimization without strict complementarity ⋮ A new superlinearly convergent norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization ⋮ Finitely convergent \(\varepsilon\)-generalized projection algorithm for nonlinear systems ⋮ A superlinearly convergent method of quasi-strongly sub-feasible directions with active set identifying for constrained optimization ⋮ Superlinearly convergent norm-relaxed SQP method based on active set identification and new line search for constrained minimax problems ⋮ Improving solver success in reaching feasibility for sets of nonlinear constraints ⋮ A superlinearly convergent method of feasible directions. ⋮ A new finitely convergent algorithm for systems of nonlinear inequalities ⋮ Sequential feasible optimal power flow in power systems ⋮ A new norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization

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• Convergence analysis of norm-relaxed method of feasible directions
• Self-tuning norm-relaxed method of feasible directions
• 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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