Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Dinh, Quoc Tran"'
Publikováno v:
Automatica 2021
In this paper, an asymptotic stability proof for a class of methods for inexact nonlinear model predictive control is presented. General Q-linearly convergent online optimization methods are considered and an asymptotic stability result is derived fo
Externí odkaz:
http://arxiv.org/abs/2004.08578
Publikováno v:
In IFAC PapersOnLine 2020 53(2):6570-6576
Many scientific and engineering applications feature nonsmooth convex minimization problems over convex sets. In this paper, we address an important instance of this broad class where we assume that the nonsmooth objective is equipped with a tractabl
Externí odkaz:
http://arxiv.org/abs/1311.1756
Source separation or demixing is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction
Externí odkaz:
http://arxiv.org/abs/1311.0258
We study the computational complexity certification of inexact gradient augmented Lagrangian methods for solving convex optimization problems with complicated constraints. We solve the augmented Lagrangian dual problem that arises from the relaxation
Externí odkaz:
http://arxiv.org/abs/1302.4355
We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local
Externí odkaz:
http://arxiv.org/abs/1301.1459
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 Lagrangian decomposition, smoothing and excessive gap. The algorithm req
Externí odkaz:
http://arxiv.org/abs/1212.4275
A new decomposition optimization algorithm, called \textit{path-following gradient-based decomposition}, is proposed to solve separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm do
Externí odkaz:
http://arxiv.org/abs/1203.3742
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 the nonconvex SDP problem by inner positive semidefinite convex appro
Externí odkaz:
http://arxiv.org/abs/1202.5488
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 a difference between two positive semidefinite convex mappings. At eac
Externí odkaz:
http://arxiv.org/abs/1109.3320