Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

Parameter control is aimed at realizing performance gains through a dynamic choice of the
parameters which determine the behavior of the underlying optimization algorithm. In the …

Problems such as bisection, graph coloring, and clique are generally believed hard in the
worst case. However, they can be solved if the input data is drawn randomly from a …

This chapter differs from the others in being concerned more with problems of counting and
integration, and correspondingly less with optimization. The problems we address still tend …

We present a unified framework for designing polynomial time approximation
schemes(PTASS) for “dense” instances of many NP-hard optimization problems, including …

In this paper, we examine a spectral clustering algorithm for similarity graphs drawn from a
simple random graph model, where nodes are allowed to have varying degrees, and we …

In this paper, we present and analyze a simple and robust spectral algorithm for the
stochastic block model with k blocks, for any k fixed. Our algorithm works with graphs having …

From the winner of the Turing Award and the Abel Prize, an introduction to computational
complexity theory, its connections and interactions with mathematics, and its central role in …

We study the expected time complexity of two graph partitioning problems: the graph
coloring and the cut into equal parts. If k= o (√ n log n), we can test whether two vertices of a …

We resolve in the affirmative a question of Boppana and Bui: whether simulated annealing
can, with high probability and in polynomial time, find the optimal bisection of a random …