Catherine C. McGeoch
Catherine C. McGeoch

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ccmatcs.amherst.edu

  Affiliation history
· Carnegie Mellon University
· Amherst College
Bibliometrics: publication history
Average citations per article8.61
Citation Count155
Publication count18
Publication years1984-2014
Available for download11
Average downloads per article550.45
Downloads (cumulative)6,055
Downloads (12 Months)189
Downloads (6 Weeks)20
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18 results found Export Results: bibtex | endnote | acmref | csv

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1
Adiabatic Quantum Computation and Quantum Annealing: Theory and Practice
July 2014
Bibliometrics:
Citation Count: 0

Adiabatic quantum computation (AQC) is an alternative to the better-known gate model of quantum computation. The two models are polynomially equivalent, but otherwise quite dissimilar: one property that distinguishes AQC from the gate model is its analog nature. Quantum annealing (QA) describes a type of heuristic search algorithm that can ...

2 published by ACM
Experimental evaluation of an adiabiatic quantum system for combinatorial optimization
May 2013 CF '13: Proceedings of the ACM International Conference on Computing Frontiers
Publisher: ACM
Bibliometrics:
Citation Count: 7
Downloads (6 Weeks): 4,   Downloads (12 Months): 30,   Downloads (Overall): 491

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This paper describes an experimental study of a novel computing system (algorithm plus platform) that carries out quantum annealing , a type of adiabatic quantum computation , to solve optimization problems. We compare this system to three conventional software solvers, using instances from three NP-hard problem domains. We also describe ...
Keywords: heuristics, D-wave, adiabatic quantum computing, quantum annealing

3
A Guide to Experimental Algorithmics
January 2012
Bibliometrics:
Citation Count: 5

Computational experiments on algorithms can supplement theoretical analysis by showing what algorithms, implementations, and speed-up methods work best for specific machines or problems. This book guides the reader through the nuts and bolts of the major experimental questions: What should I measure? What inputs should I test? How do I ...

4 published by ACM
Preface
March 2010 Journal of Experimental Algorithmics (JEA): Volume 15, 2010
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 6,   Downloads (Overall): 212

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5
Experimental algorithms for undergraduates: keynote address
January 2009 Journal of Computing Sciences in Colleges: Volume 24 Issue 3, January 2009
Publisher: Consortium for Computing Sciences in Colleges
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 2,   Downloads (Overall): 105

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6 published by ACM
Experimental algorithmics
November 2007 Communications of the ACM: Volume 50 Issue 11, November 2007
Publisher: ACM
Bibliometrics:
Citation Count: 15
Downloads (6 Weeks): 2,   Downloads (12 Months): 44,   Downloads (Overall): 1,546

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Theoretical questions and motivations, combined with empirical research methods, produce insights into algorithm and program performance.

7
Experimental algorithmics for undergraduates
June 2007 ecs'07: Experimental computer science on Experimental computer science
Publisher: USENIX Association
Bibliometrics:
Citation Count: 0


8 published by ACM
Report on education roundtable: experimentaion in the computer science curriculum
Larry Rudolph, Daniel Menasce, Fabian Bustamante, Catherine McGeoch, Ethan Miller
June 2007 ExpCS '07: Proceedings of the 2007 workshop on Experimental computer science
Publisher: ACM
Bibliometrics:
Citation Count: 0


9
Using finite experiments to study asymptotic performance
Catherine McGeoch, Peter Sanders, Rudolf Fleischer, Paul R. Cohen, Doina Precup
January 2002 Experimental algorithmics: from algorithm design to robust and efficient software
Publisher: Springer-Verlag New York, Inc.
Bibliometrics:
Citation Count: 8

In the analysis of algorithms we are interested in obtaining closed form expressions for algorithmic complexity, or at least asymptotic expressions in O(ċ)-notation. It is often possible to use experimental results to make significant progress towards this goal, although there are fundamental reasons why we cannot guarantee to obtain such ...

10 published by ACM
How to present a paper on experimental work with algorithms
Catherine C. McGeoch, Bernard M. E. Moret
December 1999 ACM SIGACT News: Volume 30 Issue 4, Dec. 1999
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 8,   Downloads (12 Months): 26,   Downloads (Overall): 602

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11 published by ACM
Emerging opportunities for theoretical computer science
September 1997 ACM SIGACT News: Volume 28 Issue 3, Sept. 1997
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 1,   Downloads (12 Months): 6,   Downloads (Overall): 282

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The principles underlying this report can be summarized as follows:1. A strong theoretical foundation is vital to computer science.2. Theory can be enriched by practice.3. Practice can be enriched by theory.4. If we are guided by (2) and (3), the value, impact, and funding of theory will be enhanced.In order ...

12
How to Find Big-Oh in Your Data Set (and How Not to)
Catherine C. McGeoch, Doina Precup, Paul R. Cohen
August 1997 IDA '97: Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Publisher: Springer-Verlag
Bibliometrics:
Citation Count: 4


13 published by ACM
Research in the curriculum, and the Web
December 1996 ACM Computing Surveys (CSUR) - Special issue: position statements on strategic directions in computing research: Volume 28 Issue 4es, Dec. 1996
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 2,   Downloads (Overall): 230

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14
Optimal sampling strategies for quicksort
December 1995 Random Structures & Algorithms: Volume 7 Issue 4, Dec. 1995
Publisher: John Wiley & Sons, Inc.
Bibliometrics:
Citation Count: 1


15 published by ACM
Analyzing algorithms by simulation: variance reduction techniques and simulation speedups
June 1992 ACM Computing Surveys (CSUR): Volume 24 Issue 2, June 1992
Publisher: ACM
Bibliometrics:
Citation Count: 26
Downloads (6 Weeks): 1,   Downloads (12 Months): 20,   Downloads (Overall): 1,150

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Although experimental studies have been widely applied to the investigation of algorithm performance, very little attention has been given to experimental method in this area. This is unfortunate, since much can be done to improve the quality of the data obtained; often, much improvement may be needed for the data ...
Keywords: statistical analysis of algorithms, transpose rule, move-to-front rule, experimental analysis of algorithms, variance reduction techniques, self-organizing sequential search

16
Experimental analysis of algorithms
January 1986
Bibliometrics:
Citation Count: 12

This thesis examines the application of experimental, statistical, and data analysis tools to problems in algorithm analysis. Note that algorithms, not programs, are studied: "results" in algorithm analysis generally refer to abstract cost functions, are independent of particular machines or implementation strategies, and express functional relationships between input parameters and ...

17 published by ACM
Amortized analyses of self-organizing sequential search heuristics
April 1985 Communications of the ACM - Lecture notes in computer science Vol. 174: Volume 28 Issue 4, April 1985
Publisher: ACM
Bibliometrics:
Citation Count: 40
Downloads (6 Weeks): 2,   Downloads (12 Months): 28,   Downloads (Overall): 878

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The performance of sequential search can be enhanced by the use of heuristics that move elements closer to the front of the list as they are found. Previous analyses have characterized the performance of such heuristics probabilistically. In this article, we use amortization to analyze the heuristics in a worst-case ...

18 published by ACM
Some unexpected expected behavior results for bin packing
J. L. Bentley, D. S. Johnson, F. T. Leighton, C. C. McGeoch, L. A. McGeoch
December 1984 STOC '84: Proceedings of the sixteenth annual ACM symposium on Theory of computing
Publisher: ACM
Bibliometrics:
Citation Count: 20
Downloads (6 Weeks): 1,   Downloads (12 Months): 14,   Downloads (Overall): 515

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We study the asymptotic expected behavior of the First Fit and First Fit Decreasing bin packing algorithms applied to items chosen uniformly from the interval (0,u], u ≤ 1. Our results indicate that the algorithms perform even better than previously expected.



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