Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

A large body of work has been devoted to defining and identifying clusters or communities in
social and information networks, ie, in graphs in which the nodes represent underlying …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

Algorithms in varied fields use the idea of maintaining a distribution over a certain set and
use the multiplicative update rule to iteratively change these weights. Their analyses are …

A large body of work has been devoted to identifying community structure in networks. A
community is often though of as a set of nodes that has more connections between its …

Finding the ground states of the Ising Hamiltonian maps to various combinatorial
optimization problems in biology, medicine, wireless communications, artificial intelligence …

We give a O (√ log n)-approximation algorithm for the sparsest cut, edge expansion,
balanced separator, and graph conductance problems. This improves the O (log n) …

We give a deterministic m^1+o(1) time algorithm that computes exact maximum flows and
minimum-cost flows on directed graphs with m edges and polynomially bounded integral …

Semidefinite programs (SDPs) are a fundamental class of optimization problems with
important recent applications in approximation algorithms, quantum complexity, robust …

This is a preliminary version of a book in progress on the theory of quantum communication.
We adopt an information-theoretic perspective throughout and give a comprehensive …