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6,920
Extremal Optimization
, 2008
"... We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successive ..."
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We explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization
Exploring Applications of Extremal Optimization
, 2009
"... Extremal Optimization (EO) is a relatively new single search-point optimization heuristic based on self-organized criticality. Unlike many traditional optimization heuristics, EO focuses on removing poor characteristics of a solution instead of preserving the good ones. This thesis will examine th ..."
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Extremal Optimization (EO) is a relatively new single search-point optimization heuristic based on self-organized criticality. Unlike many traditional optimization heuristics, EO focuses on removing poor characteristics of a solution instead of preserving the good ones. This thesis will examine
Jamming model for the extremal optimization heuristic
, 2008
"... Extremal Optimization, a recently introduced meta-heuristic for hard optimization problems, is analyzed on a simple model of jamming. The model is motivated first by the problem of finding lowest energy configurations for a disordered spin system on a fixed-valence graph. The numerical results for t ..."
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Cited by 6 (3 self)
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Extremal Optimization, a recently introduced meta-heuristic for hard optimization problems, is analyzed on a simple model of jamming. The model is motivated first by the problem of finding lowest energy configurations for a disordered spin system on a fixed-valence graph. The numerical results
The Generalized Extremal Optimization with Real Codification
"... The Generalized Extremal Optimization (GEO) is a new evolutionary algorithm devised to tackle complex optimization problems. GEO is easily implemented, does not make use of derivatives, can be applied to unconstrained or constrained problems and nonconvex or even disjoint design spaces, in the prese ..."
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The Generalized Extremal Optimization (GEO) is a new evolutionary algorithm devised to tackle complex optimization problems. GEO is easily implemented, does not make use of derivatives, can be applied to unconstrained or constrained problems and nonconvex or even disjoint design spaces
High performance scalable image compression with EBCOT
- IEEE Trans. Image Processing
, 2000
"... A new image compression algorithm is proposed, based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR ..."
Abstract
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Cited by 586 (11 self)
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scalability together with a random access property. The algorithm has modest complexity and is extremely well suited to applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisua1 metrics
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- California Institute of Technology, Pasadena
, 2008
"... Abstract. Compressive sampling offers a new paradigm for acquiring signals that are compressible with respect to an orthonormal basis. The major algorithmic challenge in compressive sampling is to approximate a compressible signal from noisy samples. This paper describes a new iterative recovery alg ..."
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Cited by 770 (13 self)
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algorithm called CoSaMP that delivers the same guarantees as the best optimization-based approaches. Moreover, this algorithm offers rigorous bounds on computational cost and storage. It is likely to be extremely efficient for practical problems because it requires only matrix–vector multiplies
The role of deliberate practice in the acquisition of expert performance
- Psychological Review
, 1993
"... The theoretical framework presented in this article explains expert performance as the end result of individuals ' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of ef ..."
Abstract
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Cited by 690 (15 self)
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of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended
Design of capacity-approaching irregular low-density parity-check codes
- IEEE TRANS. INFORM. THEORY
, 2001
"... We design low-density parity-check (LDPC) codes that perform at rates extremely close to the Shannon capacity. The codes are built from highly irregular bipartite graphs with carefully chosen degree patterns on both sides. Our theoretical analysis of the codes is based on [1]. Assuming that the unde ..."
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Cited by 588 (6 self)
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We design low-density parity-check (LDPC) codes that perform at rates extremely close to the Shannon capacity. The codes are built from highly irregular bipartite graphs with carefully chosen degree patterns on both sides. Our theoretical analysis of the codes is based on [1]. Assuming
A Singular Value Thresholding Algorithm for Matrix Completion
, 2008
"... This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of reco ..."
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Cited by 555 (22 self)
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-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative and produces a sequence of matrices {X k, Y k} and at each step, mainly performs a soft-thresholding operation on the singular values of the matrix Y k. There are two
Load Balancing in Distributed Applications Based on Extremal Optimization
, 2013
"... Abstract. The paper shows how to use Extremal Optimization in load balancing of distributed applications executed in clusters of multicore processors interconnected by a message passing network. Composed of iterative optimization phases which improve program task placement on processors, the propose ..."
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Abstract. The paper shows how to use Extremal Optimization in load balancing of distributed applications executed in clusters of multicore processors interconnected by a message passing network. Composed of iterative optimization phases which improve program task placement on processors
Results 1 - 10
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6,920
