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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&quest; We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components <i>exactly</i> by solving a very(More)
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. Recently, it has been shown that a convex program, named Principal Component Pursuit (PCP), can recover the low-rank matrix when the data matrix is corrupted by gross(More)
We study the problem of recovering the phase from magnitude measurements; specifically, we wish to reconstruct a complex-valued signal x &#x2208; &#x2102;<sup>n</sup> about which we have phaseless samples of the form y<sub>r</sub> = |&#x2329;a<sub>r</sub>, x&#x232A;|<sup>2</sup>, r = 1, ..., m (knowledge of the phase of these samples would yield a linear(More)
—Orthogonal frequency division multiplexing (OFDM) is an attractive technique for wireless communication applications. However, an OFDM signal has a large peak-to-mean envelope power ratio, which can result in significant distortion when passed through a nonlinear device, such as a transmitter power amplifier. We investigate, through extensive computer(More)
—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 large-scale optimization problems (up to 2000 real-valued variables). The proposed CCPSO2 builds on the success of an early CCPSO that employs an effective(More)
—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 optima in a dynamic environment. In the proposed species-based particle swam optimization (SPSO), the swarm population is divided into species subpopulations(More)
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighbourhood best values, for solving multimodal optimization problems. In the proposed species-based PSO (SPSO), the swarm population is divided into species sub-populations based on their similarity. Each species is grouped around a dominating particle(More)
This paper describes an extension to a speciation-based particle swarm optimizer (SPSO) to improve performance in dynamic environments. The improved SPSO has adopted several proven useful techniques. In particular, SPSO is shown to be able to adapt to a series of dynamic test cases with varying number of peaks (assuming maximization). Inspired by the(More)