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- Publications
- Influence

Robust principal component analysis?

- E. Candès, X. Li, Yuliang Ma, J. Wright
- Computer Science, Mathematics
- JACM
- 18 December 2009

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? We… Expand

Phase Retrieval via Wirtinger Flow: Theory and Algorithms

- E. Candès, X. Li, M. Soltanolkotabi
- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 3 July 2014

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

Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization

- M. Omidvar, X. Li, Yi Mei, X. Yao
- Computer Science, Mathematics
- IEEE Transactions on Evolutionary Computation
- 1 June 2014

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 idea… Expand

Stable Principal Component Pursuit

- Zihan Zhou, X. Li, J. Wright, E. Candès, Yuliang Ma
- Computer Science, Mathematics
- IEEE International Symposium on Information…
- 14 January 2010

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

Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale

- K. Tang, X. Li, P. Suganthan, Zhenyu Yang, T. Weise
- Computer Science
- 2009

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 Evolution… Expand

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Cooperatively Coevolving Particle Swarms for Large Scale Optimization

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 solving… Expand

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 of… Expand

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 multiple… Expand

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

Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'

- X. Li, A. Engelbrecht, M. G. Epitropakis
- Computer Science
- 2013

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 global… Expand

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