A new norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization

DOI10.1016/j.amc.2004.08.009zbMath1087.65062OpenAlexW2139136459MaRDI QIDQ2572648

Jin-Bao Jian, Chun-Ming Tang, Hai-Yan Zheng, Qing-Jie Hu

Publication date: 4 November 2005

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

Full work available at URL: https://doi.org/10.1016/j.amc.2004.08.009

zbMATH Keywords

algorithmnumerical experimentsNonlinear programmingGlobal convergenceStrong convergenceMethod of feasible directionMethod of strongly sub-feasible direction

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items (17)

A superlinearly convergent norm-relaxed SQP method of strongly sub-feasible directions for constrained optimization without strict complementarity ⋮ A method combining norm-relaxed QP subproblems with systems of linear equations for constrained optimization ⋮ Quadratically constraint quadratical algorithm model for nonlinear minimax problems ⋮ A new superlinearly convergent norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization ⋮ A strongly convergent norm-relaxed method of strongly sub-feasible direction for optimization with nonlinear equality and inequality constraints ⋮ Primal-dual interior point QP-free algorithm for nonlinear constrained optimization ⋮ A new sequential systems of linear equations algorithm of feasible descent for inequality constrained optimization ⋮ 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 ⋮ A generalized super-memory gradient projection method of strongly sub-feasible directions with strong convergence for nonlinear inequality constrained optimization ⋮ A norm-relaxed method of feasible directions for finely discretized problems from semi-infinite programming ⋮ A superlinearly convergent norm-relaxed method of quasi-strongly sub-feasible direction for inequality constrained minimax problems ⋮ A new norm-relaxed SQP algorithm with global convergence ⋮ A new feasible descent primal-dual interior point algorithm for nonlinear inequality constrained optimization ⋮ Generalized monotone line search SQP algorithm for constrained minimax problems ⋮ A simply sequential quadratically constrained quadratic programming method of strongly sub-feasible directions for constrained optimization ⋮ Sequential quadratically constrained quadratic programming norm-relaxed algorithm of strongly sub-feasible directions

Uses Software

• Matlab

Cites Work

• Unnamed Item
• More test examples for nonlinear programming codes
• Test examples for nonlinear programming codes
• Global convergence analysis of algorithms for finding feasible points in norm-relaxed MFD
• A generalization of the norm-relaxed method of feasible directions
• Norm-relaxed method of feasible directions for solving nonlinear programming problems
• A new SQP method of feasible directions for nonlinear programming.
• Exact penalty function algorithm with simple updating of the penalty parameter
• A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
• Combined phase I—phase II methods of feasible directions
• Rate of Convergence of a Class of Methods of Feasible Directions
• On the Convergence of Some Feasible Direction Algorithms for Nonlinear Programming
• On the rate of convergence of certain methods of centers
• A superlinearly convergent method of feasible directions.

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