Smoothed analysis

Known as: Smoothed complexity 
Smoothed analysis is a way of measuring the complexity of an algorithm. It gives a more realistic analysis of the practical performance of the… (More)
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Topic mentions per year

Topic mentions per year

2001-2017
05101520012017

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Highly Cited
2014
Highly Cited
2014
Low rank decomposition of tensors is a powerful tool for learning generative models. The uniqueness results that hold for tensors… (More)
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2011
2011
The <i>k</i>-means method is one of the most widely used clustering algorithms, drawing its popularity from its speed in practice… (More)
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2011
2011
Many algorithms perform very well in practice, but have a poor worst-case performance. The reason for this discrepancy is that… (More)
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2009
2009
The k-means method is a widely used clustering algorithm. One of its distinguished features is its speed in practice. Its worst… (More)
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2007
2007
The probabilistic roadmap algorithm is a leading heuristic for robot motion planning. It is extremely efficient in practice, yet… (More)
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2007
2007
  • Bodo Mantheya, Rüdiger Reischukb
  • 2007
Binary search trees are one of the most fundamental data structures. While the height of such a tree may be linear in the worst… (More)
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Highly Cited
2006
Highly Cited
2006
The authors advance significantly beyond the recent progress on the algorithmic complexity of Nash equilibria by solving two… (More)
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2006
2006
Smoothed analysis of complexity bounds and condition numbers has been done, so far, on a case by case basis. In this paper we… (More)
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2005
2005
We present a probabilistic analysis of integer linear programs (ILPs). More specifically, we study ILPs in a so-called smoothed… (More)
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Highly Cited
2001
Highly Cited
2001
We introduce the <italic>smoothed analysis of algorithms</italic>, which is a hybrid of the worst-case and average-case analysis… (More)
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