The Lov\'asz theta function $\theta(G)$ of a graph $G$ has attracted a lot of attention for its connection with diverse issues, such as communicating without errors and computing large cliques in graphs. Indeed this function enjoys the remarkable property of being computable in polynomial time, despite being sandwitched between clique ... more >>>

We present the first randomized polynomial-time simplex algorithm for linear programming. Like the other known polynomial-time algorithms for linear programming, its running time depends polynomially on the number of bits used to represent its input.

We begin by reducing the input linear program to a special form in which we ... more >>>

We consider the problem of finding a $k$-vertex ($k$-edge)
connected spanning subgraph $K$ of a given $n$-vertex graph $G$
while minimizing the maximum degree $d$ in $K$. We give a
polynomial time algorithm for fixed $k$ that achieves an $O(\log
n)$-approximation. The only known previous polynomial algorithms
achieved degree $d+1$ ... more >>>

We study linear programming relaxations of Vertex Cover and Max Cut
arising from repeated applications of the ``lift-and-project''
method of Lovasz and Schrijver starting from the standard linear
programming relaxation.

For Vertex Cover, Arora, Bollobas, Lovasz and Tourlakis prove that
the integrality gap remains at least $2-\epsilon$ after
$\Omega_\epsilon(\log n)$ ... more >>>

We introduce a new approach to characterizing the unobserved portion of a distribution, which provides sublinear-sample additive estimators for a class of properties that includes entropy and distribution support size. Together with the lower bounds proven in the companion paper [29], this settles the longstanding question of the sample complexities ... more >>>

Two graphs with adjacency matrices $\mathbf{A}$ and $\mathbf{B}$ are isomorphic if there exists a permutation matrix $\mathbf{P}$ for which the identity $\mathbf{P}^{\mathrm{T}} \mathbf{A} \mathbf{P} = \mathbf{B}$ holds. Multiplying through by $\mathbf{P}$ and relaxing the permutation matrix to a doubly stochastic matrix leads to the notion of fractional isomorphism. We show ... more >>>