Concave programming for minimizing the zero-norm over polyhedral sets
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DOI: 10.1007/s10589-008-9202-9
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Cited by:
- Dongdong Zhang & Shaohua Pan & Shujun Bi & Defeng Sun, 2023. "Zero-norm regularized problems: equivalent surrogates, proximal MM method and statistical error bound," Computational Optimization and Applications, Springer, vol. 86(2), pages 627-667, November.
- Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2023. "Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines," Computational Optimization and Applications, Springer, vol. 86(2), pages 745-766, November.
- Enrico Civitelli & Matteo Lapucci & Fabio Schoen & Alessio Sortino, 2021. "An effective procedure for feature subset selection in logistic regression based on information criteria," Computational Optimization and Applications, Springer, vol. 80(1), pages 1-32, September.
- Behzad Pirouz & Behrouz Pirouz, 2023. "Multi-Objective Models for Sparse Optimization in Linear Support Vector Machine Classification," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
- Angelo Ciccazzo & Gianni Di Pillo & Vittorio Latorre, 2015. "A SVM Surrogate Model Based Method for Yield Optimization in Electronic Circuit Design," DIAG Technical Reports 2015-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
- Le Thi, H.A. & Pham Dinh, T. & Le, H.M. & Vo, X.T., 2015. "DC approximation approaches for sparse optimization," European Journal of Operational Research, Elsevier, vol. 244(1), pages 26-46.
- Immanuel M. Bomze & Francesco Rinaldi & Damiano Zeffiro, 2021. "FrankâWolfe and friends: a journey into projection-free first-order optimization methods," 4OR, Springer, vol. 19(3), pages 313-345, September.
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Keywords
Zero-norm; Concave optimization; Frank-Wolfe algorithm; Feature selection;All these keywords.
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