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430
Smoothed analysis: an attempt to explain the behavior of algorithms in practice
 COMMUN. ACM
, 2009
"... Many algorithms and heuristics work well on real data, despite having poor complexity under the standard worstcase measure. Smoothed analysis [36] is a step towards a theory that explains the behavior of algorithms in practice. It is based on the assumption that inputs to algorithms are subject to ..."
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Cited by 30 (0 self)
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Many algorithms and heuristics work well on real data, despite having poor complexity under the standard worstcase measure. Smoothed analysis [36] is a step towards a theory that explains the behavior of algorithms in practice. It is based on the assumption that inputs to algorithms are subject
Features of similarity.
 Psychological Review
, 1977
"... Similarity plays a fundamental role in theories of knowledge and behavior. It serves as an organizing principle by which individuals classify objects, form concepts, and make generalizations. Indeed, the concept of similarity is ubiquitous in psychological theory. It underlies the accounts of stimu ..."
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Cited by 1455 (2 self)
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, errors of substitution, and correlation between occurrences. Analyses of these data attempt to explain the observed similarity relations and to capture the underlying structure of the objects under study. The theoretical analysis of similarity relations has been dominated by geometric models
Coil sensitivity encoding for fast MRI. In:
 Proceedings of the ISMRM 6th Annual Meeting,
, 1998
"... New theoretical and practical concepts are presented for considerably enhancing the performance of magnetic resonance imaging (MRI) by means of arrays of multiple receiver coils. Sensitivity encoding (SENSE) is based on the fact that receiver sensitivity generally has an encoding effect complementa ..."
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Cited by 193 (3 self)
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analysis of error propagation in creating the raw map: where ⍀ is a parameter reflecting the degree of smoothing, d x,y denotes the pixel value at position x,y of the denominator image used for preparing the raw map, and ⌽ x,y is an ''object indicator'' map: ⌽ x,y ϭ 1 where object
Smoothed Analysis: Motivation and Discrete Models
 Proc. of WADS 2003
, 2003
"... Abstract. In smoothed analysis, one measures the complexity of algorithms assuming that their inputs are subject to small amounts of random noise. In an earlier work (Spielman and Teng, 2001), we introduced this analysis to explain the good practical behavior of the simplex algorithm. In this paper, ..."
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Cited by 6 (0 self)
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Abstract. In smoothed analysis, one measures the complexity of algorithms assuming that their inputs are subject to small amounts of random noise. In an earlier work (Spielman and Teng, 2001), we introduced this analysis to explain the good practical behavior of the simplex algorithm. In this paper
Worst case instances are fragile: average case and smoothed competitive analysis of algorithms
 PHD THESIS, UNIVERSITÄT DES SAARLANDES
, 2004
"... We describe three results in this thesis. We first present a heuristic improvement for a shortest path problem, which we termed singlesource manytargets shortest path problem. In this problem, we need to compute a shortest path from a source node to a node that belongs to a designated target set. ..."
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Cited by 1 (0 self)
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complexity to the area of online algorithms. Smoothed complexity has been introduced by Spielman and Teng to explain the behavior of algorithms performing well in practice while having a poor worst case complexity. The idea is to add some noise to the initial input instances by perturbing the input values
Seismic Ray Theory
, 2001
"... Introduction T he propagation of seismic body waves in complex, laterally varying 3D layered structures is a complicated process. Analytical solutions of the elastodynamic equations for such types of media are not known. The most common approaches to the investigation of seismic wavefields in such ..."
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Cited by 131 (4 self)
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and travel times but also the raytheory amplitudes, synthetic seismograms, and particle ground motions. These methods are well suited to the study of seismic wavefields in smoothly inhomogeneous 3D media composed of thick layers separated by smoothly curved interfaces. The highfrequency asymptotic methods
Average Case and Smoothed Competitive Analysis of the . . .
, 2003
"... In this paper we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng [23] to explain the behaviour of algorithms that work well in practice while performing very poorly from a worst case analysis point of view. We apply ..."
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Cited by 23 (6 self)
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In this paper we introduce the notion of smoothed competitive analysis of online algorithms. Smoothed analysis has been proposed by Spielman and Teng [23] to explain the behaviour of algorithms that work well in practice while performing very poorly from a worst case analysis point of view. We
Viz: A Visual Analysis Suite for Explaining Local Search Behavior
 IN: USER INTERFACE SOFTWARE AND TECHNOLOGY
, 2006
"... NPhard combinatorial optimization problems are common in real life. Due to their intractability, local search algorithms are often used to solve such problems. Since these algorithms are heuristicbased, it is hard to understand how to improve or tune them. We propose an interactive visualization t ..."
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Cited by 9 (5 self)
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tool, VIZ, meant for understanding the behavior of local search. VIZ uses animation of abstract search trajectories with other visualizations which are also animated in a VCRlike fashion to graphically playback the algorithm behavior. It combines generic visualizations applicable on arbitrary
Smoothed Analysis of the Successive Shortest Path Algorithm
, 2013
"... The minimumcost flow problem is a classic problem in combinatorial optimization with various applications. Several pseudopolynomial, polynomial, and strongly polynomial algorithms have been developed in the past decades, and it seems that both the problem and the algorithms are well understood. Ho ..."
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Cited by 2 (1 self)
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an exponential worstcase running time, seems to outperform the strongly polynomial MinimumMean Cycle Canceling algorithm. To explain this discrepancy, we study the SSP algorithm in the framework of smoothed analysis and establish a bound of O(mnφ(m + n log n)) for its smoothed running time. This shows
Towards Explaining the Speed of kMeans
"... The kmeans method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worstcase runningtime. To explain the speed of the kmeans method, a smoothed analysis has been conducted. We sketch this smoothed analysis and a generalization to ..."
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The kmeans method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worstcase runningtime. To explain the speed of the kmeans method, a smoothed analysis has been conducted. We sketch this smoothed analysis and a generalization
Results 1  10
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