A strongly convergent norm-relaxed method of strongly sub-feasible direction for optimization with nonlinear equality and inequality constraints
The authors investigate a general constrained optimization problem where the constraints are of nonlinear equality or nonlinear inequality type. In the first section the methodological background is outlined and the necessary definitions are presented. The second section then presents the main part of the paper, which combines the norm-relaxed method of feasible direction with the method of sub-feasible direction. Sections 3 and 4 then study convergence properties of the proposed algorithm where several theorems are presented and demonstrated with proof. The article concludes with a section containing the results of the performed numerical experimentation and a useful list of references.
scientific article; zbMATH DE number 5083739
scientific article