We survey optimization problems that involve the cardinality of variable vectors in
constraints or the objective function. We provide a unified viewpoint on the general problem …

Sparse approximation addresses the problem of approximately fitting a linear model with a
solution having as few non-zero components as possible. While most sparse estimation …

The problem of recovering a structured signal x∈ C p from a set of dimensionality-reduced
linear measurements b= Ax arises in a variety of applications, such as medical imaging …

Bayesian coresets have emerged as a promising approach for implementing scalable
Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of …

Label distribution learning methods effectively address the label ambiguity problem and
have achieved great success in image emotion analysis. However, these methods ignore …

This paper studies a general form problem in which a lower bounded continuously
differentiable function is minimized over a block separable set incorporating a group sparsity …

As an information carrier with rich semantics, images contain more sentiment than texts and
audios. So, images are increasingly used by people to express their opinions and …

We consider the problem of recovering fusion frame sparse signals from incomplete
measurements. These signals are composed of a small number of nonzero blocks taken …

This paper describes a simple framework for structured sparse recovery based on convex
optimization. We show that many structured sparsity models can be naturally represented by …

This paper studies high-order evaluation complexity for partially separable convexly-
constrained optimization involving non-Lipschitzian group sparsity terms in a nonconvex …