Combining the norm-relaxed sequential quadratic programming (SQP) method and the idea of method of quasi-strongly sub-feasible directions (MQSSFD) with active set identification technique, a new SQP algorithm for solving nonlinear inequality constrained optimization is proposed. Unlike the previous
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A superlinearly convergent strongly sub-feasible SSLE-type algorithm with working set for nonlinearly constrained optimization
โ Scribed by Jin-bao Jian; Wei-xin Cheng
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 857 KB
- Volume
- 225
- Category
- Article
- ISSN
- 0377-0427
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In this work, combining the properties of the generalized super-memory gradient projection methods with the ideas of the strongly sub-feasible directions methods, we present a new algorithm with strong convergence for nonlinear inequality constrained optimization. At each iteration, the proposed alg