A superlinearly convergent feasible descent algorithm for nonlinear optimization
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| Title: | A superlinearly convergent feasible descent algorithm for nonlinear optimization |
|---|---|
| Authors: | Jian, Jinbao |
| Publisher Information: | Wuhan University, Wuchang |
| Subject Terms: | superlinearly convergent feasible descent algorithm, Applications of renewal theory (reliability, demand theory, etc.), Nonlinear programming |
| Description: | The author considers the following problem: \((p)\) \(\min \{f_0 (x)|x \in R\}\), where \(R = \{x \in E^n |f_i (x) \leq 0\), \(j \in L_1\); \(f_j (x) = 0\), \(j \in L_2\}\), and discusses optimization with nonlinear equality and inequality constraints. The author presents a new algorithm possessing the following properties: (1) The algorithm is a feasible descent method for the expansive problems and the parameter adjusts automatically only for finite time; (2) only one quadratic program needs to be solved at each iteration; (3) It superlinearly converges to the solution of the original problem under some suitable assumption. |
| Document Type: | Article |
| File Description: | application/xml |
| Access URL: | https://zbmath.org/883157 |
| Accession Number: | edsair.c2b0b933574d..0c8c93bc143425ade3a62a914d217c67 |
| Database: | OpenAIRE |
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