»ã±¨±êÌâ (Title)£ºAdaptive Dimension Reduction for Overlapping Group Sparsity£¨³Áµþ×éÏ¡ÉÙÐÔµÄ×ÔÊÊӦάÊýÔ¼¼ò£©

»ã±¨ÈË (Speaker)£ºÁº¾­Î³ ¸±½ÌÊÚ£¨ÉϺ£½»Í¨´óѧ£©

»ã±¨¹¦·ò (Time)£º2025Äê12ÔÂ11ÈÕ£¨ÖÜÎ壩9:30

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿GJ303

Ô¼ÇëÈË(Inviter)£ºÖÜ°²ÍÞ

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»ã±¨ÌáÒª£ºTypical dimension reduction techniques for sparse optimization involve screening strategies based on a dual certificate derived from the first-order optimality condition, approximating the gradients or exploiting some inherent low dimensional structure that an optimization algorithm promotes. Screening rules for overlapping group lasso are generally less developed because the subgradient structure is more complex and the link between sparsity pattern and the dual vector is generally indirect. In this talk, I will present a new strategy for certifying the support of the overlapping group lasso and demonstrate how this can be applied significantly accelerate the performance of numerical methods.