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Dario Bini

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3 papers
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3

FOCS Conference 1992 Conference Paper

Improved Parallel Polynomial Division and Its Extensions

  • Dario Bini
  • Victor Y. Pan

The authors compute the first N coefficients of the reciprocal r(x) of a given polynomial p(x), (r(x)p(x)=1 mod x/sup N/, p(0) not=0), by using, under the PRAM arithmetic models, O(h log N) time-steps and O((N/h)(1+2/sup -h/log/sup (h)/ N)) processors, for any h, h=1, 2, .. ., log/sup */ N, provided that O(logm) steps and m processors suffice to perform DFT on m points and that log/sup (0)/ N=N, log/sup (h)/ N=log/sub 2/log/sup (h-1)/N, h=1, .. ., log/sup */N, log/sup */N=max(h: log/sup (h)/N>0). The same complexity estimates apply to some other computations, such as the division with a remainder of two polynomials of degrees O(N) and the inversion of an N*N triangular Toeplitz matrix. They also show how to extend the techniques to parallel implementation of other recursive processes, such as the evaluation modulo x/sup N/ of the m-th root, p(x)/sup 1/m/, of p(x) (for any fixed natural m), for which we need O(log N log log N) time-steps and O(N/log log N) processors. The paper demonstrates some new techniques of supereffective slowdown of parallel algebraic computations, which they combine with a technique of stream contraction. >

TCS Journal 1983 Journal Article

On commutativity and approximation

  • Dario Bini

The action of commutativity and approximation is analyzed for some problems in Computational Complexity. Lower bound criteria to the approximate complexity are given in terms of border rank and commulative border rank of a given tensor. Upper bounds for the approximate complexity of the matrix-vector product are given. In particular, 1 2 m(n+1) multiplications are necessary and sufficient to approximate n × m matrix-vector product; 6 multiplications are sufficient (5 are needed) to approximate a 2 × 2 matrix product by using commutativity. An application to polynomial evaluation shows that 1 2 n+2 multiplications are sufficient to approximate any n-degree polynomial at a point. For what concerns matrix multiplication complexity a number θ is introduced such that θ⩽ω (ω is the exponent of matrix multiplication complexity). This number measures the degree of complexity of the best commutative approximate algorithm for matrix multiplication. The bound θ⩽2. 3211…and conditions under which θ=ω are shown.