GPGPU for Difficult Black-box Problems

AbstractDifficult black-box problems arise in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove that GPGPU outperforms CPU in some memetic algorithms which can be used as a part of hybrid algorithm of finding near optimal solutions of Golomb Ruler problem. The presented research is a part of building heterogenous parallel algorithm for difficult black-box Golomb Ruler problem

Disjoint sets of distinct sum sets

AbstractAn (h, J)-distinct sum set is a set of J integers such that all sums of h elements (repetitions allowed) are distinct. An (h, I, J)-set of disjoint distinct sum sets is a set of I disjoint (h, J)-distinct sum sets with positive elements. A number of constructions of such sets are given

Disjoint sets of distinct sum sets

AbstractAn (h, J)-distinct sum set is a set of J integers such that all sums of h elements (repetitions allowed) are distinct. An (h, I, J)-set of disjoint distinct sum sets is a set of I disjoint (h, J)-distinct sum sets with positive elements. A number of constructions of such sets are given

On the Achievable Rate Region of Sequential Decoding for a Class of Multiaccess Channels

The achievable-rate region of sequential decoding for the class of pairwise reversible multiaccess channels is determined. This result is obtained by finding tight lower bounds to the average list size for the same class of channels. The average list size is defined as the expected number of incorrect messages that appear, to a maximum-likelihood decoder, to be at least as likely as the correct message. The average list size bounds developed here may be of independent interest, with possible applications to list-decoding schemes. © 1990 IEE