- Exact Low-rank Matrix Recovery via Nonconvex Mp-Minimization Lingchen Kong(lchkongbjtu.edu.cn) Naihua Xiu(nhxiubjtu.edu.cn) Abstract: The low-rank matrix recovery (LMR) arises in many fields such as signal and image processing, statistics, computer vision, system identification and control, and it is NP-hard. It is known that under some restricted isometry property (RIP) conditions we can obtain the exact low-rank matrix solution by solving its convex relaxation, the nuclear norm minimization. In this paper, we consider the nonconvex relaxations by introducing $M_p$-norm (\$0 Keywords: low-rank matrix recovery, nonconvex Mp-minimization, restricted isometry constant, Category 1: Nonlinear Optimization Citation: Beijing Jiaotong University, April/2011 Download: [PDF]Entry Submitted: 04/21/2011Entry Accepted: 04/21/2011Entry Last Modified: 04/21/2011Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.