Optimization underpins many of the challenges that science and technology face on a daily
basis. Recent years have witnessed a major shift from traditional optimization paradigms …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

In this overview article, we review the literature on design and analysis of recursive
algorithms for reconstructing a time sequence of sparse signals from compressive …

This paper describes two algorithms for state reconstruction from sensor measurements that
are corrupted with sparse, but otherwise arbitrary,“noise.” These results are motivated by the …

Regression analysis based methods have shown strong robustness and achieved great
success in face recognition. In these methods, convex-norm and nuclear norm are usually …

The combination of photoacoustic (PA) imaging and ultrasound localization microscopy
(ULM) with microbubbles has great potential in various fields such as oncology …

In an underdetermined mixture system with n unknown sources, it is a challenging task to
separate these sources from their m observed mixture signals, where mn By exploiting the …

This paper develops online algorithms to track solutions of time-varying constrained
optimization problems. Particularly, resembling workhorse Kalman filtering-based …

The low-rank matrix reconstruction (LRMR) approach is widely used in direction-of-arrival
(DOA) estimation. As the rank norm penalty in an LRMR is NP-hard to compute, the nuclear …

This paper considers sequential compressed acquisition and progressive reconstruction of
spatially and temporally correlated sensor data streams in wireless sensor networks (WSNs) …