Download Hypergeometric Summation: An Algorithmic Approach to by Wolfram Koepf PDF

By Wolfram Koepf

Smooth algorithmic concepts for summation, such a lot of which have been brought within the Nineteen Nineties, are built the following and punctiliously applied within the computing device algebra process Maple™.

The algorithms of Fasenmyer, Gosper, Zeilberger, Petkovšek and van Hoeij for hypergeometric summation and recurrence equations, effective multivariate summation in addition to q-analogues of the above algorithms are lined. related algorithms relating differential equations are thought of. An identical thought of hyperexponential integration because of Almkvist and Zeilberger completes the book.

The blend of those effects offers orthogonal polynomials and (hypergeometric and q-hypergeometric) certain services an outstanding algorithmic beginning. therefore, many examples from this very lively box are given.

The fabrics coated are appropriate for an introductory path on algorithmic summation and should entice scholars and researchers alike.

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Extra resources for Hypergeometric Summation: An Algorithmic Approach to Summation and Special Function Identities (2nd Edition) (Universitext)

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Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA. Koza, John R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts. , and Streeter, Matthew J. (2003). Evolving inventions. Scientific American. , and Keane, Martin A. (2004). The challenge of producing human-competitive results by means of genetic and evolutionary computation. In Menon, Anil, editor, Frontiers of Evolutionary Computation, volume 11 of Genetic Algorithms And Evolutionary Computation Series, chapter 9, pages 73–99.

While the different settings between mutation and crossover suggest a higher rate of convergence (and more focus on a small area of high fitness) with higher crossover, rates, the causes of this difference are by no means clear. Is it that the higher crossover rate find, and combine building blocks more readily? Or does the higher mutation rate in Figure 2-5 disrupt building blocks as they accumulate? One question that comes to mind is the structural differences of mutational alteration compared to crossover.

And Worzel, W. P. (2007). Program structurefitness disconnect and its impact on evolution in GP. , Soule, Terence, and Worzel, Bill, editors, Genetic Programming Theory and Practice V, Genetic and Evolutionary Computation, chapter 9, pages 145– 160. Springer, Ann Arbor. , Radman, Miroslav, and Ramsden, Jeremy J. (2006). From artificial evolution to computational evolution: a research agenda. In Nature Reviews: Genetics, volume 7, page 729—735. , and Francone, Frank D. (1998). Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications.

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