Branching Programs and Binary Decision Diagrams: Theory and by Ingo Wegener

By Ingo Wegener

Finite features (in specific, Boolean capabilities) play a primary function in laptop technological know-how and discrete arithmetic. This e-book describes representations of Boolean capabilities that experience small measurement for plenty of very important features and which permit effective paintings with the represented services. The illustration dimension of vital and chosen features is predicted, higher and reduce certain suggestions are studied, effective algorithms for operations on those representations are awarded, and the boundaries of these strategies are thought of. This e-book is the 1st finished description of thought and purposes. examine parts like complexity idea, effective algorithms, information constructions, and discrete arithmetic will enjoy the idea defined during this publication. the implications defined inside of have functions in verification, computer-aided layout, version checking, and discrete arithmetic. this can be the single ebook to enquire the illustration dimension of Boolean features and effective algorithms on those representations.

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Extra resources for Branching Programs and Binary Decision Diagrams: Theory and Applications (Monographs on Discrete Mathematics and Applications)

Example text

Is of instructions of type Ij — (opj,Fj,Sj), where opj € B^ is some binary Boolean operation and Fj (F = first) and Sj (S = second) are elements from {0, l , x i , . . ,x n ,/i,... ,/j_i}. The inputs 0, l,xi,... ,x n represent the corresponding functions. If Fj represents / and Sj represents g, then Ij represents h := opj(f,g). The size of the circuit is equal to the number s of its instructions. (ii) A formula on Xn = {xi,... ,x n } is a circuit, where each instruction Ik occurs only once as an argument of another instruction Ij.

Then i £ [n — log(n + 1 — logn), n — log(n +1 — log n) + 1). 3. Upper Bound Techniques for BPs 31 the left and right borders of the considered interval. If x = n — log(n + l — logn), then 2X = 2 n /(n + 1 - logn) = (1 4- o(l))2 n /n and 22"~* = 2 • 2 n /n. If x = n- log(n + 1 - logn) + 1, then 21 = (2 + o(l))2 n /n and 22"~x = o(2n/n). This proves the theorem. n Gropl, Promel, and Srivastav (1998) have investigated the behavior of 2X + 22" x more exactly. 1 is even an OBDD (see Section 3). Using the full power of general BPs, Breitbart, Hunt III, and Rosenkrantz (1995) have proved an upper bound of (l + o(l))2 n /n on the BP size of f e Bn.

Since NAND(x,y) =xy, we get DNF(Ffc) < DNF(F/ l _ 1 ) 2 . Now the bounds follow by induction. 8 for S = 0. 1,£44+2,141+3,0:41+4}. We calculate exactly the sum c^ of the absolute values of all Fourier coefficients for dense sets. 375. For larger h, we describe NAND algebraically in the (+1, —l)-representation. Then NAND(i,y) = \(xy -x-y-1). Let F^\ and F^\ be copies of F/,_i on disjoint sets of variables. Then Since F^_\ and F^_\ depend only on half the variables, they do not distribute anything to the Fourier coefficients for dense sets.

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