New Upper and Lower Bound Sifting Iterations

نویسنده

  • ZARATHUSTRA BRADY
چکیده

with fκ(s) as large as possible (resp. Fκ(s) as small as possible) given that the above inequality holds for all choices of A satisfying (1). Selberg [3] has shown (in a much more general context) that the functions fκ(s), Fκ(s) are continuous, monotone, and computable for s > 1, and that they tend to 1 exponentially as s goes to infinity. Let β = β(κ) be the infimum of s such that fκ(s) > 0. Selberg [3] has shown that we have κ e(1− 1 κ )2 < β < 2κ+ 0.4454,

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Model Sifting Problem of Selberg

We study a model sifting problem introduced by Selberg, in which all of the primes have roughly the same size. We show that the Selberg lower bound sieve is asymptotically optimal in this setting, and we use this to give a new lower bound on the sifting limit βκ in terms of the sifting dimension κ. We also show that one can use a rounding procedure to improve on the Selberg lower bound sieve by...

متن کامل

A New Lower Bound for Completion Time Distribution Function of Stochastic PERT Networks

In this paper, a new method for developing a lower bound on exact completion time distribution function of stochastic PERT networks is provided that is based on simplifying the structure of this type of network. The designed mechanism simplifies network structure by arc duplication so that network distribution function can be calculated only with convolution and multiplication. The selection of...

متن کامل

A New Lower Bound for Completion Time Distribution Function of Stochastic PERT Networks

In this paper, a new method for developing a lower bound on exact completion time distribution function of stochastic PERT networks is provided that is based on simplifying the structure of this type of network. The designed mechanism simplifies network structure by arc duplication so that network distribution function can be calculated only with convolution and multiplication. The selection of...

متن کامل

Modified frame algorithm and its convergence acceleration by Chebyshev method

The aim of this paper is to improve the convergence rate of frame algorithm based on Richardson iteration and Chebyshev methods. Based on Richardson iteration method, we first square the existing convergence rate of frame algorithm which in turn the number of iterations would be bisected and increased speed of convergence is achieved. Afterward, by using Chebyshev polynomials, we improve this s...

متن کامل

Exact and Heuristic Minimization of Determinant Decision Diagrams

Determinant Decision Diagram (DDD) is a variant of binary decision diagrams (BDDs) for representing symbolic matrix determinants and cofactors in symbolic circuit analysis. DDD-based symbolic analysis algorithms have time and space complexities proportional to the number of DDD vertices. Inspired by the ideas of Rudell, Drechsler, et. al. on BDD minimization, we present lowerbound based exact a...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017