Non-convex regularization and accelerated gradient algorithm for sparse portfolio selection

In portfolio optimization, non-convex regularization has recently been recognized as an important approach to promote sparsity, while countervailing the shortcomings of convex penalty. In this paper, we customize the non-convex piecewise quadratic approximation (PQA) function considering the backgro...

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Vydané v:	Optimization methods & software Ročník 38; číslo 2; s. 434 - 456
Hlavní autori:	Li, Qian, Zhang, Wei, Wang, Guoqiang, Bai, Yanqin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Taylor & Francis 04.03.2023 Taylor & Francis Ltd
Predmet:	accelerated proximal algorithm Algorithms Empirical analysis linear convergence Mathematical models non-convex regularization Optimization Parameters Portfolio management Regularization Sharpe ratio Sparse portfolio selection Sparsity turnover
ISSN:	1055-6788, 1029-4937
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