20,125 research outputs found
Neural computation of arithmetic functions
A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n -bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions
Twenty-Five Comparators is Optimal when Sorting Nine Inputs (and Twenty-Nine for Ten)
This paper describes a computer-assisted non-existence proof of nine-input
sorting networks consisting of 24 comparators, hence showing that the
25-comparator sorting network found by Floyd in 1964 is optimal. As a
corollary, we obtain that the 29-comparator network found by Waksman in 1969 is
optimal when sorting ten inputs.
This closes the two smallest open instances of the optimal size sorting
network problem, which have been open since the results of Floyd and Knuth from
1966 proving optimality for sorting networks of up to eight inputs.
The proof involves a combination of two methodologies: one based on
exploiting the abundance of symmetries in sorting networks, and the other,
based on an encoding of the problem to that of satisfiability of propositional
logic. We illustrate that, while each of these can single handed solve smaller
instances of the problem, it is their combination which leads to an efficient
solution for nine inputs.Comment: 18 page
Picture-Hanging Puzzles
We show how to hang a picture by wrapping rope around n nails, making a
polynomial number of twists, such that the picture falls whenever any k out of
the n nails get removed, and the picture remains hanging when fewer than k
nails get removed. This construction makes for some fun mathematical magic
performances. More generally, we characterize the possible Boolean functions
characterizing when the picture falls in terms of which nails get removed as
all monotone Boolean functions. This construction requires an exponential
number of twists in the worst case, but exponential complexity is almost always
necessary for general functions.Comment: 18 pages, 8 figures, 11 puzzles. Journal version of FUN 2012 pape
An Elegant Algorithm for the Construction of Suffix Arrays
The suffix array is a data structure that finds numerous applications in
string processing problems for both linguistic texts and biological data. It
has been introduced as a memory efficient alternative for suffix trees. The
suffix array consists of the sorted suffixes of a string. There are several
linear time suffix array construction algorithms (SACAs) known in the
literature. However, one of the fastest algorithms in practice has a worst case
run time of . The problem of designing practically and theoretically
efficient techniques remains open. In this paper we present an elegant
algorithm for suffix array construction which takes linear time with high
probability; the probability is on the space of all possible inputs. Our
algorithm is one of the simplest of the known SACAs and it opens up a new
dimension of suffix array construction that has not been explored until now.
Our algorithm is easily parallelizable. We offer parallel implementations on
various parallel models of computing. We prove a lemma on the -mers of a
random string which might find independent applications. We also present
another algorithm that utilizes the above algorithm. This algorithm is called
RadixSA and has a worst case run time of . RadixSA introduces an
idea that may find independent applications as a speedup technique for other
SACAs. An empirical comparison of RadixSA with other algorithms on various
datasets reveals that our algorithm is one of the fastest algorithms to date.
The C++ source code is freely available at
http://www.engr.uconn.edu/~man09004/radixSA.zi
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