By Jürgen Gerhard

ISBN-10: 3540240616

ISBN-13: 9783540240617

ISBN-10: 3540301372

ISBN-13: 9783540301370

This paintings brings jointly streams in desktop algebra: symbolic integration and summation at the one hand, and quick algorithmics nevertheless. in lots of algorithmically orientated components of desktop technological know-how, theanalysisof- gorithms–placedintothe limelightbyDonKnuth’stalkat the 1970ICM –provides a crystal-clear criterion for fulfillment. The researcher who designs an algorithmthat is quicker (asymptotically, within the worst case) than any prior strategy gets fast grati?cation: her end result should be well-known as priceless. sadly, the drawback is that such effects come alongside particularly occasionally, regardless of our greatest efforts. an alternate review strategy is to run a brand new set of rules on examples; this has its noticeable difficulties, yet is typically the simplest we will do. George Collins, one of many fathers of laptop algebra and a good experimenter,wrote in 1969: “I imagine this demonstrates back basic research is usually extra revealing than a ream of empirical information (although either are important). ” inside machine algebra, a few components have characteristically the previous technique, significantly a few components of polynomial algebra and linear algebra. different parts, corresponding to polynomial approach fixing, haven't but been amenable to this - proach. the standard “input dimension” parameters of desktop technology look insufficient, and even supposing a few usual “geometric” parameters were identi?ed (solution measurement, regularity), now not all (potential) significant development may be expressed during this framework. Symbolic integration and summation were in an analogous state.

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**Extra info for Modular Algorithms in Symbolic Summation and Symbolic Integration**

**Example text**

2. 3. 080 is written in C++. 0c for integer and polynomial arithmetic, parts of which are described in Shoup (1995). It uses Karatsuba’s (Karatsuba & Ofman 1962) method for multiplying large integers. Next we discuss three methods employing fast polynomial arithmetic. D. Paterson & Stockmeyer’s (1973) method: We assume that (n + 1) = m2 is a square (padding f with leading zeroes if necessary), and write f = (i) mi x , with polynomials f (i) ∈ R[x] of degree less than m for 0≤i

Then f (x + b) ∞ ≤ f (x + b) 1 ≤ (|b| + 1)n f 1 ≤ (n + 1)(|b| + 1)n f ∞ . For b = ±1, the following sharper bound is valid: f (x ± 1) Proof. Let f = f (x + b) 1 0≤i≤n ∞ ≤ f (x ± 1) ≤ 2n+1 f ∞ . fi xi . Then fi · (x + b)i = 1 ≤ 0≤i≤n 1 |fi |(1 + |b|)i ≤ (|b| + 1)n f 0≤i≤n Moreover, we have f (x + b) 1 ≤ f (1 + |b|)i = f ∞ 0≤i≤n and the claim for |b| = 1 follows. ✷ ∞ (1 + |b|)n+1 − 1 , |b| 1 . 1 Computing Taylor Shifts For all b, B, n ∈ N>0 , the polynomial f = B bound within a factor of at most n + 1: f (x + b) 1 (b + 1)i ≥ f =B 0≤i≤n ∞ (b 43 xi achieves the first + 1)n .

24. Consider the following random experiment. An urn contains b black and w white balls, and we draw k ≤ w + b balls without replacement. (i) If w ≥ b, then the probability that at most k/2 balls are white is at most 1/2. (ii) If w ≥ 4b and k ≥ 8, then the probability that at most k/2 balls are white is at most 1/4. Proof. 31 in von zur Gathen & Gerhard (1999) for a proof of (i). Let X denote the random variable counting the number of white balls after k trials. Then X has a hypergeometric distribution prob(X = i) = w i b k−i w+b k for 0 ≤ i ≤ k, with mean µ = EX = k · prob(X = k) = 0≤i≤k 4 kw ≥ k.

### Modular Algorithms in Symbolic Summation and Symbolic Integration by Jürgen Gerhard

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