报告主题：A revised gradient descent algorithm for linearly constrained lp minimization with p ∈ (0,1)
报告人：Shan Jiang 博士 （美国北卡州立大学）
报告时间：2018年12月19日（周三）15:00
报告地点：校本部G508
邀请人：白延琴
报告摘要：In this paper, we study the linearly constrained lp minimization problem with p ∈ (0,1). Unlike former works in the literature that propose ε-KKT points through relaxed optimality conditions, here we define a scaled KKT condition that is not relaxed. A revised gradient descent algorithm is proposed to search for points satisfying the proposed condition. The convergency proofs with complexity analysis of the proposed algorithm are provided. Computational experiments support that the proposed algorithm is capable of achieving better sparse recovery with far less computational time compared to state-of-the-art interior-point based algorithm.
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