51 citations · 63 across the 7 of their papers we have counts for
7 papers
Convexity in source separation: Models, geometry, and algorithms
Michael B. McCoy, Volkan Cevher, Quoc Tran Dinh +2
Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separat…
Fast Inexact Decomposition Algorithms For Large-Scale Separable Convex Optimization
Quoc Tran Dinh, Ion Necoara, Moritz Diehl
In this paper we propose a new inexact dual decomposition algorithm for solving separable convex optimization problems. This algorithm is a combination of three techniques: dual La…
An Inner Convex Approximation Algorithm for BMI Optimization and Applications in Control
Quoc Tran Dinh, Wim Michiels, Moritz Diehl
In this work, we propose a new local optimization method to solve a class of nonconvex semidefinite programming (SDP) problems. The basic idea is to approximate the feasible set of…
An inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization
Quoc Tran Dinh, Ion Necoara, Carlo Savorgnan +1
In this paper, we propose an inexact perturbed path-following algorithm in the framework of Lagrangian dual decomposition for solving large-scale structured convex optimization pro…
Combining Convex-Concave Decompositions and Linearization Approaches for solving BMIs, with application to Static Output Feedback
Quoc Tran Dinh, Suat Gumussoy, Wim Michiels +1
A novel optimization method is proposed to minimize a convex function subject to bilinear matrix inequality (BMI) constraints. The key idea is to decompose the bilinear mapping as…
Adjoint-based predictor-corrector sequential convex programming for parametric nonlinear optimization
Q. Tran Dinh, C. Savorgnan, M. Diehl
This paper proposes an algorithmic framework for solving parametric optimization problems which we call adjoint-based predictor-corrector sequential convex programming. After prese…