Search Results - subject:"Proximal gradient method"

Sparse recovery via partial regularization : models, theory, and algorithms

Lu, Zhaosong; Li, Xiaorui - In: Mathematics of operations research 43 (2018) 4, pp. 1290-1316

Persistent link: https://www.econbiz.de/10011957061

An adaptive accelerated proximal gradient method and its homotopy continuation for sparse optimization

Lin, Qihang; Xiao, Lin - In: Computational Optimization and Applications 60 (2015) 3, pp. 633-674

<Para ID="Par1">We consider optimization problems with an objective function that is the sum of two convex terms: one is smooth and given by a black-box oracle, and the other is general but with a simple, known structure. We first present an accelerated proximal gradient (APG) method for problems where the...</para>

Persistent link: https://www.econbiz.de/10011241264

On proximal gradient method for the convex problems regularized with the group reproducing kernel norm

Zhang, Haibin; Wei, Juan; Li, Meixia; Zhou, Jie; Chao, … - In: Journal of Global Optimization 58 (2014) 1, pp. 169-188

approach to solve such problems is by the proximal gradient method. In this paper we derive and study theoretically the …

Persistent link: https://www.econbiz.de/10010845793

Efficient algorithms for robust and stable principal component pursuit problems

Aybat, Necdet; Goldfarb, Donald; Ma, Shiqian - In: Computational Optimization and Applications 58 (2014) 1, pp. 1-29

The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision, image processing and web data ranking. It has been shown that under certain...

Persistent link: https://www.econbiz.de/10010847445