Determining Optimal Rates for Communication for Omniscience

TL;DR

This paper presents algorithms to determine optimal communication rates for achieving omniscience among users observing correlated sources, minimizing total communication in both asymptotic and non-asymptotic models.

Contribution

It introduces a modified decomposition algorithm and a sum-rate increment algorithm, along with a fusion method for the nesting algorithm, improving computational efficiency.

Findings

Algorithms determine minimum sum-rate in polynomial time.

Fusion method reduces computational complexity.

Separable convex minimization can be decomposed by the fundamental partition.

Abstract

This paper considers the communication for omniscience (CO) problem: A set of users observe a discrete memoryless multiple source and want to recover the entire multiple source via noise-free broadcast communications. We study the problem of how to determine an optimal rate vector that attains omniscience with the minimum sum-rate, the total number of communications. The results cover both asymptotic and non-asymptotic models where the transmission rates are real and integral, respectively. We propose a modified decomposition algorithm (MDA) and a sum-rate increment algorithm (SIA) for the asymptotic and non-asymptotic models, respectively, both of which determine the value of the minimum sum-rate and a corresponding optimal rate vector in polynomial time. For the coordinate saturation capacity (CoordSatCap) algorithm, a nesting algorithm in MDA and SIA, we propose to implement it by a fusion method and show by experimental results that this fusion method contributes to a reduction in computation complexity. Finally, we show that the separable convex minimization problem over the optimal rate vector set in the asymptotic model can be decomposed by the fundamental partition, the optimal partition of the user set that determines the minimum sum-rate, so that the problem can be solved more efficiently.