3 citations · 6 across the 8 of their papers we have counts for
8 papers
Full characterization of core for nonlinear optimization games
Donglei Du, Qizhi Fang, Bin Liu +2
We fully characterize the core of a broad class of nonlinear games by identifying a suitable relaxation for inherent nonlinearity, directly generalizing the linear frameworks in th…
Parallel algorithms for maximizing one-sided -smooth function
Hongxiang Zhang, Yukun Cheng, Chenchen Wu +2
In this paper, we study the problem of maximizing a monotone normalized one-sided -smooth ( for short) function , subject to a convex polytope. This problem was first…
Maximizing Modular plus Non-monotone Submodular Functions
Xin Sun, Chenchen Wu, Dachuan Xu +1
The research problem in this work is the relaxation of maximizing non-negative submodular plus modular with the entire real number domain as its value range over a family of down-c…
An improved approximation algorithm for maximizing a DR-submodular function over a convex set
Donglei Du, Zhicheng Liu, Chenchen Wu +2
Maximizing a DR-submodular function subject to a general convex set is an NP-hard problem arising from many applications in combinatorial optimization and machine learning. While i…
Outliers Detection Is Not So Hard: Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques
Yishui Wang, Rolf H. Möhring, Chenchen Wu +2
In this paper, we consider two types of robust models of the -median/-means problems: the outlier-version (-MedO/-MeaO) and the penalty-version (-MedP/-MeaP), in…
Too Much Information Kills Information: A Clustering Perspective
Yicheng Xu, Vincent Chau, Chenchen Wu +3
Clustering is one of the most fundamental tools in the artificial intelligence area, particularly in the pattern recognition and learning theory. In this paper, we propose a simple…