In the ad-hoc radio network model, nodes communicate with their neighbors via radio signals, without knowing the topology of the underlying digraph. We study the information gathering problem, where each node has a piece of information called a rumor, and the objective is to transmit all rumors to the designated target node. For the model without any collision detection we provide an O˜(n1.5) deterministic protocol, significantly improving the trivial bound of O(n2). We also consider a model with a mild form of collision detection, where a node receives a 1-bit acknowledgment if its transmission was received by at least one out-neighbor. For this model we give an O˜(n) deterministic protocol for information gathering in acyclic graphs.

We consider the performance of two classic approximation algorithms which work by scanning the input and greedily constructing a solution. We investigate whether running these algorithms on a random permutation of the input can increase their performance ratio. We obtain the following results: 1. Johnson's approximation algorithm for MAX-SAT is one of the first approximation algorithms to be rigorously analyzed. It has been shown that the performance ratio of this algorithm is 2/3. We show that when executed on a random permutation of the variables, the performance ratio of this algorithm is improved to 2/3 + c for some c > 0 This resolves an open problem of Chen, Friesen and Zhang [JCSS 1999]. (See also the paper by Poloczek and Schnitger in these proceedings for related results on this algorithm and its variants). 2. Motivated by the above improvement, we consider the performance of the greedy algorithm for MAX-CUT whose performance ratio is 1/2. Our hope was that running the greedy algorithm on a random permutation of the vertices would result in a 1/2 + c approximation algorithm. However, it turns out that in this case the performance of the algorithm remains 1/2. This resolves an open problem of Mathieu and Schudy [SODA 2008].

We show that the absolute value of the determinant of a matrix with random independent (but not necessarily i.i.d.) entries is strongly concentrated around its mean. As an application, we show that Godsil–Gutman and Barvinok estimators for the permanent of a strictly positive matrix give subexponential approximation ratios with high probability. A positive answer to the main conjecture of the paper would lead to polynomial approximation ratios in the above problem.