Speaker: Anna Blasiak, Cornell Title: Flow-Cut Gaps and Network Coding Date/time: 11am-noon on Thursday February 7 Place: 402 in the CS building Abstract: The classic max-flow min-cut theorem states that in a network with one source and one sink, the amount of information that can be sent from the source to the sink is equal to the minimum capacity of a set of edges separating the source from the sink. This equivalence allows for efficient computation of both parameters. Modifying traffic demands, constraints, and optimization functions yields many variations of the max-flow problem. In all of these variations there is a corresponding cut minimization problem whose optimal value serves as an upper bound on the max-flow. But unlike the single source-sink version, the cut upper bound is rarely equal to the max-flow. The flow-cut gap, the worst possible ratio between the max-flow rate and the cut upper bound, is an important area of study for developing approximation algorithms and gaining a better understanding of network flow. Related to the max-flow problem is the network coding rate, the maximum transmission rate of information in networks whose nodes can perform nontrivial encoding and decoding operations on incoming messages. The network coding rate is always as big as the max-flow rate and can be much larger. A spectacular result of network coding theory is that in the multicast problem, a setting with a large flow-cut gap, the coding rate is always equal to the cut bound. This talk considers the relations between the flow rate, coding rate, and cut bounds for multicommodity flow problems. I present new coding-cut gaps, the worst-case ratio between the coding rate and cut upper bound. Further, I show that there exist paradigms apart from multicast in which coding can bridge the flow-cut gap. Specifically, in the network that provides the largest known separation (Saks et al.) between the maximum multicommodity flow and multicut, the coding rate is equal to the multicut. This is joint work with Robert Kleinberg (Cornell University) and Eyal Lubetzky (Microsoft Research Redmond).