A feasible SQP-GS algorithm for nonconvex, nonsmooth constrained optimization

The gradient sampling (GS) algorithm for minimizing a nonconvex, nonsmooth function was proposed by Burke et al. (SIAM J Optim 15:751–779, 2005 ), whose most interesting feature is the use of randomly sampled gradients instead of subgradients. In this paper, combining the GS technique with the sequ...

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Vydané v:	Numerical algorithms Ročník 65; číslo 1; s. 1 - 22
Hlavní autori:	Tang, Chun-ming, Liu, Shuai, Jian, Jin-bao, Li, Jian-ling
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.01.2014 Springer Nature B.V
Predmet:	Algebra Algorithms Computer Science Constraints Convergence Iterative methods Mathematical analysis Mathematical models Numeric Computing Numerical Analysis Optimization Original Paper Quadratic programming Sampling Sequences Theory of Computation Gradient sampling 49M30 Nonsmooth optimization 90C30 Feasible algorithm Sequential quadratic programming
ISSN:	1017-1398, 1572-9265
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