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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Veröffentlicht in:	Optimization methods & software Jg. 38; H. 2; S. 434 - 456
Hauptverfasser:	Li, Qian, Zhang, Wei, Wang, Guoqiang, Bai, Yanqin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Abingdon Taylor & Francis 04.03.2023 Taylor & Francis Ltd
Schlagworte:	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
Online-Zugang:	Volltext
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