24 citations · 35 across the 6 of their papers we have counts for
6 papers
A Scale Invariant Approach for Sparse Signal Recovery
Yaghoub Rahimi, Chao Wang, Hongbo Dong +1
In this paper, we study the ratio of the and norms, denoted as , to promote sparsity. Due to the non-convexity and non-linearity, there has been little atten…
Compact Disjunctive Approximations to Nonconvex Quadratically Constrained Programs
Hongbo Dong, Yunqi Luo
Decades of advances in mixed-integer linear programming (MILP) and recent development in mixed-integer second-order-cone programming (MISOCP) have translated very mildly to progres…
On the exact recovery of sparse signals via conic relaxations
Hongbo Dong
In this note we compare two recently proposed semidefinite relaxations for the sparse linear regression problem by Pilanci, Wainwright and El Ghaoui (Sparse learning via boolean re…
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selection
Hongbo Dong, Kun Chen, Jeff Linderoth
Variable selection is a fundamental task in statistical data analysis. Sparsity-inducing regularization methods are a popular class of methods that simultaneously perform variable…
Relaxing nonconvex quadratic functions by multiple adaptive diagonal perturbations
Hongbo Dong
The current bottleneck of globally solving mixed-integer (non-convex) quadratically constrained problem (MIQCP) is still to construct strong but computationally cheap convex relaxa…
Reduced rank regression via adaptive nuclear norm penalization
Kun Chen, Hongbo Dong, Kung-Sik Chan
Adaptive nuclear-norm penalization is proposed for low-rank matrix approximation, by which we develop a new reduced-rank estimation method for the general high-dimensional multivar…