• Publications
  • Influence
Traffic managements for household travels in congested morning commute
Due to the high car ownership cost or car ownership restrictions in many major cities, household travels, which include multiple trips for all the household members, become very common. One typicalExpand
  • 16
  • 3
On the convergence of the direct extension of ADMM for three-block separable convex minimization models with one strongly convex function
TLDR
We show that when one function in the objective is strongly convex, the penalty parameter and the operators in the linear equality constraint are appropriately restricted, it is sufficient to guarantee the convergence of the direct extension of ADMM. Expand
  • 53
  • 2
A proximal point algorithm revisit on the alternating direction method of multipliers
The alternating direction method of multipliers (ADMM) is a benchmark for solving convex programming problems with separable objective functions and linear constraints. In the literature it has beenExpand
  • 41
  • 2
  • PDF
The direct extension of ADMM for three-block separable convex minimization models is convergent when one function is strongly convex
The alternating direction method of multipliers (ADMM) is a benchmark for solving a two-block linearly constrained convex minimization model whose objective function is the sum of two functionsExpand
  • 35
  • 2
  • PDF
On the O(1/t) convergence rate of the projection and contraction methods for variational inequalities with Lipschitz continuous monotone operators
TLDR
Nemirovski’s analysis (SIAM J. Optim. 15:229–251, 2005) indicates that the extragradient method has the O(1/t) convergence rate. Expand
  • 59
  • 1
  • PDF
Comparison of several fast algorithms for projection onto an ellipsoid
TLDR
We rewrite the convex projection problem as a constrained convex optimization problem with separable objective functions, which enables the use of the alternating direction method of multipliers (ADMM). Expand
  • 11
  • 1
Nonnegative tensor factorizations using an alternating direction method
The nonnegative tensor (matrix) factorization finds more and more applications in various disciplines including machine learning, data mining, and blind source separation, etc. In computation, theExpand
  • 10
  • 1
Generalized ADMM with optimal indefinite proximal term for linearly constrained convex optimization
We consider the generalized alternating direction method of multipliers (ADMM) for linearly constrained convex optimization. Many problems derived from practical applications have showed that usuallyExpand
  • 6
  • 1
  • PDF
An ADM-based splitting method for separable convex programming
TLDR
We consider the convex minimization problem with linear constraints and a block-separable objective function which is represented as the sum of three functions without coupled variables. Expand
  • 28
An improved first-order primal-dual algorithm with a new correction step
TLDR
In this paper, we propose a new correction strategy for some first-order primal-dual algorithms arising from solving, e.g., total variation image restoration. Expand
  • 10
...
1
2
3
4
...