Are you an EPFL student looking for a semester project?

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Concept

Communication complexity

Summary

In theoretical computer science, communication complexity studies the amount of communication required to solve a problem when the input to the problem is distributed among two or more parties. The study of communication complexity was first introduced by Andrew Yao in 1979, while studying the problem of computation distributed among several machines. The problem is usually stated as follows: two parties (traditionally called Alice and Bob) each receive a (potentially different) n-bit string x and y. The goal is for Alice to compute the value of a certain function, f(x, y), that depends on both x and y, with the least amount of communication between them. While Alice and Bob can always succeed by having Bob send his whole n-bit string to Alice (who then computes the function f), the idea here is to find clever ways of calculating f with fewer than n bits

Official source

https://en.wikipedia.org/wiki/Communication_complexity

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (19)

Related publications

Related people (1)

Related people

Related units (1)

Related units

Related concepts (3)

Related concepts

Related courses (5)

Computing is nowadays distributed over several machines, in a local IP-like network, a cloud or a P2P network. Failures are common and computations need to proceed despite partial failures of machines or communication links. This course will study the foundations of reliable distributed computing.

With the advent of multiprocessors, it becomes crucial to master the underlying algorithmics of concurrency. The objective of this course is to study the foundations of concurrent algorithms and in particular the techniques that enable the construction of robust such algorithms.

This course introduces the basics of cryptography. We review several types of cryptographic primitives, when it is safe to use them and how to select the appropriate security parameters. We detail how they work and sketch how they can be implemented.

Related courses

Related lectures (6)

Related lectures

Unambiguous DNFs and Alon-Saks-Seymour

Mika Tapani Göös, Siddhartha Jain

We exhibit an unambiguous k-DNF formula that requires CNF width (Omega) over tilde (k(2)), which is optimal up to logarithmic factors. As a consequence, we get a near-optimal solution to the Alon-Saks-Seymour problem in graph theory (posed in 1991), which asks: How large a gap can there be between the chromatic number of a graph and its biclique partition number? Our result is also known to imply several other improved separations in query and communication complexity.

IEEE COMPUTER SOC2022

Fast Byzantine Agreement

Rachid Guerraoui, Florian Huc

This paper presents the first probabilistic Byzantine Agreement algorithm whose communication and time complexities are poly-logarithmic. So far, the most effective probabilistic Byzantine Agreement algorithm had communication complexity and time complexity. Our algorithm is based on a novel, unbalanced, almost everywhere to everywhere Agreement protocol which is interesting in its own right.

2013

Additive Combinatorics and Discrete Logarithm Based Range Protocols

Rafik Chaabouni, Helger Lipmaa

We show how to express an arbitrary integer interval

I = [0, H]

as a sumset

I = \sum_{i=1}^\ell G_i * [0, u - 1] + [0, H']

of smaller integer intervals for some small values

\ell

u

, and

H' < u - 1

, where

b * A = \{b a : a \in A\}

and

A + B = \{a + b : a \in A \wedge b \in B\}

. We show how to derive such expression of

I

as a sumset for any value of

1 < u < H

, and in particular, how the coefficients

G_i

can be found by using a nontrivial but efficient algorithm. This result may be interesting by itself in the context of additive combinatorics. Given the sumset-representation of

I

, we show how to decrease both the communication complexity and the computational complexity of the recent pairing-based range proof of Camenisch, Chaabouni and shelat from ASIACRYPT 2008 by a factor of

2

. Our results are important in applications like e-voting where a voting server has to verify thousands of proofs of e-vote correctness per hour. Therefore, our new result in additive combinatorics has direct relevance in practice.

Springer2010