Sparse optimization has attracted increasing attention in numerous areas such as compressed sensing, financial optimization and image processing. In this paper, we first consider a special class of l0 constrained optimization problems, which involves box constraints and a singly linear...

Optimal investment strategies for enhanced indexation problems have attracted considerable attentions over the last decades in the field of fund management. In this paper, a featured difference from the existing literature is that our main concern of the investigation is the development of a...

In this paper, we propose and analyze Adaptive Projected Gradient Thresholding (APGT) methods for finding sparse solutions of the underdetermined linear system with equality and box constrains. The general convergence will be demonstrated, and in addition the bound of the number of iterations is...

In this paper, we investigate an enhanced indexation methodology using robust Conditional Value-at-Risk (CVaR) and group-sparse optimization. A featured difference from the existing literatures is to describe the tail risk using the worst-case CVaR of excess returns (WCVaR-ER), and the process...

In this paper, we investigate an enhanced indexation methodology using robust Conditional Value-at-Risk (CVaR) and group-sparse optimization. A featured difference from the existing literatures is to describe the tail risk using the worst-case CVaR of excess returns (WCVaR-ER), and the process...

A continuous approach using NCP function for approximating the solution of the max-cut problem is proposed. The max-cut problem is relaxed into an equivalent nonlinearly constrained continuous optimization problem and a feasible direction method without line searches is presented for generating...