Weighted Matching in the Semi-Streaming Model

TL;DR

This paper improves the approximation ratio for weighted matching in the semi-streaming model from 5.828 to 5.585, advancing algorithms for processing large graphs with limited memory in a single pass.

Contribution

It presents a new semi-streaming algorithm that achieves a better approximation ratio for weighted matching, reducing previous bounds.

Findings

Improved approximation ratio from 5.828 to 5.585

Efficient one-pass semi-streaming algorithm for large graphs

Applicable to massive graphs with limited memory constraints

Abstract

We reduce the best known approximation ratio for finding a weighted matching of a graph using a one-pass semi-streaming algorithm from 5.828 to 5.585. The semi-streaming model forbids random access to the input and restricts the memory to O(n*polylog(n)) bits. It was introduced by Muthukrishnan in 2003 and is appropriate when dealing with massive graphs.