Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “average-case analysis”

average case analysis

Cezar Câmpeanu, Nelma Moreira, Rogério Reis et al. · 2011

Abstract. In this paper we investigate from a statistical point of view the expected compressionratiobetweenthesizeofaminimalDeterministicFiniteCoverAutomaton(DFCA) and the size of the minimal corresponding Determinitic Finite Automaton (DFA). Using sound statistical methods, we extend the experimental study done in [16], thus obtaining a much better picture of the compression power of DFCAs. We compute the expected ratio for the family of all finite languages, but also for various subfamilies of finite languages, such as prefix, suffix-free languages prefix and suffix closed languages, or (un)balanced languages. We also give an example of a family for which the expected compression ratio is very high. 1

Cited by 0

Average-Case Analysis

David Simchi‐Levi, Xin Chen, Julien Bramel · Springer series in operations research/Springer series in operations research and financial engineering · 2013

Cited by 9

Distritrack: Automated Average-Case Analysis

David Hickey · 2007

MOQA is a language specifically designed to facilitate average-case execution time analysis. It incorporates some innovative techniques to make this possible. Following on from this, a tool called Distritrack has been built to perform the analysis of MOQA programs, adhering strictly to the language specification.

Cited by 6

Average Case Analysis of Multichannel Thresholding

Rémi Gribonval, Boris Mailhé, Holger Rauhut et al. · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2007

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dictionary. Numerical simulations confirm our theoretical findings and show that p-thresholding is an interesting low complexity alternative to simultaneous greedy or convex relaxation algorithms for processing sparse multichannel signals with balanced coefficients.

Cited by 25Open access

Average-case analysis for combinatorial problems

Abraham D. Flaxman · 2006

This thesis considers the average case analysis of algorithms, focusing primarily on NP-hard combinatorial optimization problems. It includes a catalog of distributions frequently used in average-case analysis and a collection of mathematical tools that have been useful in studying these distributions. The bulk of the thesis consists of case-studies in average-case analysis of algorithms. Algorithms for 3-SAT, Subset Sum, Strong Connectivity, Stochastic Minimum Spanning Tree, and Uncapacitated Facility Location are analyzed on random instances.

Cited by 0

Average case analysis of unification algorithms

Institut National de Recherche en Informatique et en Automatique (INRIA), 78 - Rocquencourt (France). Unite de Recherche de Rocquencourt · OpenGrey (Institut de l'Information Scientifique et Technique) · 1990

Cited by 0

Average-case analysis of multiuser detectors

Toshiyuki Tanaka · 2002

Based on correspondence between code-division multiple-access (CDMA) multiuser detection problem and statistical mechanics, I present a novel analytical result on the average-case performance of direct sequence binary phase key shifting CDMA multiuser detectors, in the large-system limit and under some simplifying assumptions, using tools borrowed from statistical mechanics.

Cited by 7

Average Case Analysis of Brzozowski's Algorithm

Sven De Felice, Cyril Nicaud · International Journal of Foundations of Computer Science · 2016

We analyze the average complexity of Brzozowski's minimization algorithm for distributions of deterministic automata with a small number of final states. We show that, as in the case of the uniform distribution, the average complexity is super-polynomial even if we consider random deterministic automata with only one final state. Such results were only known for distributions where the expected number of final states was linear in the number of states.

Cited by 0Open access

Average-Case Analysis of Online Topological Ordering

Deepak Ajwani, Tobias Friedrich · ArXiv.org · 2008

Many applications like pointer analysis and incremental compilation require maintaining a topological ordering of the nodes of a directed acyclic graph (DAG) under dynamic updates. All known algorithms for this problem are either only analyzed for worst-case insertion sequences or only evaluated experimentally on random DAGs. We present the first average-case analysis of online topological ordering algorithms. We prove an expected runtime of O(n^2 polylog(n)) under insertion of the edges of a complete DAG in a random order for the algorithms of Alpern et al. (SODA, 1990), Katriel and Bodlaender (TALG, 2006), and Pearce and Kelly (JEA, 2006). This is much less than the best known worst-case bound O(n^{2.75}) for this problem.

Cited by 0Open access

Average-case analysis of some plurality algorithms

Laurent Alonso, Edward M. Reingold · ACM Transactions on Algorithms · 2009

Given a set of n elements, each of which is colored one of c colors, we must determine an element of the plurality (most frequently occurring) color by pairwise equal/unequal color comparisons of elements. We focus on the expected number of color comparisons when the c n colorings are equally probable. We analyze an obvious algorithm, showing that its expected performance is c 2 + c − 2/2 c n − O ( c 2 ), with variance Θ( c 2 n ). We present and analyze an algorithm for the case c = 3 colors whose average complexity on the 3 n equally probable inputs is 7083/5425 n + O (√ n ) = 1.3056… n + O (√ n ), substantially better than the expected complexity 5/3 n + O (1) = 1.6666… n + O (1) of the obvious algorithm. We describe a similar algorithm for c =4 colors whose average complexity on the 4 n equally probable inputs is 761311/402850 n + O (log n ) = 1.8898… n + O (log n ), substantially better than the expected complexity 9/4 n + O (1) = 2.25 n + O (1) of the obvious algorithm.

Cited by 7