• Publications
  • Influence
Robust principal component analysis?
This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? WeExpand
  • 4,756
  • 1001
Phase Retrieval via Wirtinger Flow: Theory and Algorithms
We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x ∈ ℂn about which we have phaseless samples of the form yr =Expand
  • 770
  • 123
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the ideaExpand
  • 356
  • 64
Stable Principal Component Pursuit
In this paper, we study the problem of recovering a low-rank matrix (the principal components) from a high-dimensional data matrix despite both small entry-wise noise and gross sparse errors.Expand
  • 432
  • 62
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale
In the past decades, different kinds of metaheuristic optimization algorithms [1, 2] have been developed; Simulated Annealing (SA) [3, 4], Evolutionary Algorithms (EAs) [5–7], Differential EvolutionExpand
  • 564
  • 55
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
  • X. Li, X. Yao
  • Mathematics, Computer Science
  • IEEE Transactions on Evolutionary Computation
  • 1 April 2012
This paper presents a new cooperative coevolving particle swarm optimization (CCPSO) algorithm in an attempt to address the issue of scaling up particle swarm optimization (PSO) algorithms in solvingExpand
  • 506
  • 53
A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization
  • X. Li
  • Computer Science
  • GECCO
  • 12 July 2003
This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use ofExpand
  • 472
  • 47
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
  • Daniel Parrott, X. Li
  • Computer Science, Mathematics
  • IEEE Transactions on Evolutionary Computation
  • 1 August 2006
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multipleExpand
  • 483
  • 45
Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology
  • X. Li
  • Computer Science, Mathematics
  • IEEE Transactions on Evolutionary Computation
  • 1 February 2010
Niching is an important technique for multimodal optimization. Most existing niching methods require specification of certain niching parameters in order to perform well. These niching parameters,Expand
  • 321
  • 42
Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'
Evolutionary Algorithms (EAs) in their original forms are usually designed for locating a single global solution. These algorithms typically converge to a single solution because of the globalExpand
  • 152
  • 35