We present a simple semi-streaming algorithm for $(1-\epsilon)$-approximation of bipartite matching in $O(\log{\!(n)}/\epsilon)$ passes. This matches the performance of state-of-the-art "$\epsilon$-efficient" algorithms, while being considerably simpler. The algorithm relies on a "white-box" application of the multiplicative weight update method with a self-contained primal-dual analysis that can be of independent interest. To show case this, we use the same ideas, alongside standard tools from matching theory, to present an equally simple semi-streaming algorithm for $(1-\epsilon)$-approximation of weighted matchings in general (not necessarily bipartite) graphs, again in $O(\log{\!(n)}/\epsilon)$ passes
Abstract. We study the communication complexity and streaming complexity of approximating unweighted...
Given a graph G, it is well known that any maximal matching M in G is at least half the size of a ma...
We formalize a potentially rich new streaming model, the semi-streaming model, that we believe is ne...
We reduce the best known approximation ratio for finding a weighted matching of a graph using a o...
We present an improved deterministic algorithm for Maximum Cardinality Matching on general graphs in...
We present the first deterministic 1+eps approximation algorithm for finding a large matching in a b...
We consider the maximum matching problem in the semi-streaming model formalized by Feigenbaum, Kanna...
Abstract. In the semi-streaming model, an algorithm receives a stream of edges of a graph in arbitra...
Multi-pass streaming algorithm for Maximum Matching have been studied since more than 15 years and v...
The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is...
We study the maximum matching problem in the random-order semi-streaming setting. In this problem, t...
We present a (4 + epsilon) approximation algorithm for weighted graph matching which applies in the ...
This report presents algorithms for finding large matchings in the streaming model. In this model, a...
In a recent breakthrough, Paz and Schwartzman (SODA'17) presented a single-pass (2+epsilon)-approxim...
We study the maximum weight matching problem in the semi-streaming model, and improve on the current...
Abstract. We study the communication complexity and streaming complexity of approximating unweighted...
Given a graph G, it is well known that any maximal matching M in G is at least half the size of a ma...
We formalize a potentially rich new streaming model, the semi-streaming model, that we believe is ne...
We reduce the best known approximation ratio for finding a weighted matching of a graph using a o...
We present an improved deterministic algorithm for Maximum Cardinality Matching on general graphs in...
We present the first deterministic 1+eps approximation algorithm for finding a large matching in a b...
We consider the maximum matching problem in the semi-streaming model formalized by Feigenbaum, Kanna...
Abstract. In the semi-streaming model, an algorithm receives a stream of edges of a graph in arbitra...
Multi-pass streaming algorithm for Maximum Matching have been studied since more than 15 years and v...
The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is...
We study the maximum matching problem in the random-order semi-streaming setting. In this problem, t...
We present a (4 + epsilon) approximation algorithm for weighted graph matching which applies in the ...
This report presents algorithms for finding large matchings in the streaming model. In this model, a...
In a recent breakthrough, Paz and Schwartzman (SODA'17) presented a single-pass (2+epsilon)-approxim...
We study the maximum weight matching problem in the semi-streaming model, and improve on the current...
Abstract. We study the communication complexity and streaming complexity of approximating unweighted...
Given a graph G, it is well known that any maximal matching M in G is at least half the size of a ma...
We formalize a potentially rich new streaming model, the semi-streaming model, that we believe is ne...