《Python算法教程》——2.8 参考资料

简介:

本节书摘来自异步社区《Python算法教程》一书中的第2章,第2.8节,作者[挪威]Magnus Lie Hetland(赫特兰), 凌杰 译,更多章节内容可以访问云栖社区“异步社区”公众号查看。

2.8 参考资料

  • Acton, F. S. (2005). Real Computing Made Real: Preventing Errors in Scientific and Engineering Calculations. Dover Publications, Inc.
  • Bang-Jensen, J. and Gutin, G. (2002). Digraphs: Theory, Algorithms and Applications. Springer.
  • Bast, H. and Weber, I. (2005). Don’t compare averages. In Nikoletseas, S. E., editor, WEA, volume 3503 of Lecture Notes in Computer Science, pages 67–76. Springer.
  • Bondy, J. A. and Murty, U. S. R. (2008). Graph Theory . Springer.
  • Brandstädt, A., Le, V. B., and Spinrad, J. P. (1999). Graph Classes: A Survey. SIAM Monographs on Discrete Mathematics and Applications. Society for Industrial and Applied Mathematics.
  • Citron, D., Hurani, A., and Gnadrey, A. (2006). The harmonic or geometric mean: Does it really matter? ACM SIGARCH Computer Architecture News, 34(4):18–25.
  • Diestel, R. (2005). Graph Theory , third edition. Springer.
  • Fleming, P. J. and Wallace, J. J. (1986). How not to lie with statistics: The correct way to summarize benchmark results. Commun. ACM, 29(3):218–221.
  • Goldberg, D. (1991). What every computer scientist should know about floating-point arithmetic. ACM Computing Surveys (CSUR), 23(1):5–48. http://docs.sun.com/source/806-3568/ncg_goldberg.html.
  • Hromkovič, J. (2009). Algorithmic Adventures: From Knowledge to Magic. Springer.
  • Knuth, D. E. (1976). Big Omicron and big Omega and big Theta. ACM SIGACT News, 8(2):18–24.
  • Lewis, H. R. and Papadimitriou, C. H. (1998). Elements of the Theory of Computation , second edition. Prentice Hall, Inc.
  • Martelli, A., Ravenscroft, A., and Ascher, D., editors (2005). Python Cookbook , second edition. O’Reilly & Associates, Inc.
  • Massart, D. L., Smeyers-Verbeke, J., Capron, X., and Schlesier, K. (2005). Visual presentation of data by means of box plots. LCGC Europe , 18:215–218.
  • McGeoch, C., Sanders, P., Fleischer, R., Cohen, P. R., and Precup, D. (2002). Using finite experiments to study asymptotic performance. Lecture Notes in Computer Science , 2547:94–126.
  • Moret, B. M. E. (2002). Towards a discipline of experimental algorithmics. In Data Structures, Near Neighbor Searches, and Methodology: Fifth and Sixth DIMACS Implementation Challenges, volume 59 of DIMACS: Series in Discrete Mathematics and Theoretical Computer Science, pages 197–214. Americal American Mathematical Society.
  • Petzold, C. (2008). The Annotated Turing: A Guided Tour Through Alan Turing’s Historic Paper on Computability and the Turing Machine. Wiley Publishing, Inc.
  • Salzberg, S. (1997). On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining and Knowledge Discovery , 1(3):317–328.
  • Sanders, P. (2002). Presenting data from experiments in algorithmics. Lecture Notes in Computer Science , 2547:181–196.
  • Spinrad, J. P. (2003). Efficient Graph Representations. Fields Institute Monographs. American Mathematical Society.
  • Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society , s2-42(1):230–265.
  • Vaingast, S. (2009). Beginning Python Visualization: Crafting Visual Transformation Scripts. Apress.
  • West, D. B. (2001). Introduction to Graph Theory , second edition. Prentice Hall, Inc.
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