**Monday, May 30, 2011**

Technische Universität Berlin

Fakultät II, Institut für Mathematik

Str. des 17. Juni 136

10623 Berlin

room MA 041

** Lecture - 14:15**

*Abstract:*

The traveling salesman problem (TSP) is the most famous problem in discrete optimization. Given $n$ cities and the costs $c_{ij}$ for traveling from city $i$ to city $j$ for all $i,j$, the goal of the problem is to find the least expensive tour that visits each city exactly once and returns to its starting point. We consider cases in which the costs are symmetric and obey the triangle inequality. In 1954, Dantzig, Fulkerson, and Johnson introduced a linear programming relaxation of the TSP now known as the subtour LP, and used it to find the optimal solution to a 48-city instance. Ever since then, the subtour LP has been used extensively to find optimal solutions to TSP instances, and it is known to give extremely good lower bounds on the length of an optimal tour.

Nevertheless, the quality of the subtour LP bound is poorly understood from a theoretical point of view. For 30 years it has been known that it is at least 2/3 times the length of an optimal tour for all instances of the problem, and it is known that there are instances such that it is at most 3/4 times the length of an optimal tour, but no progress has been made in 30 years in tightening these bounds.

In this talk we will review some of the results that are known about the subtour LP, and give some new results that refine our understanding in some cases. In particular, we have been able to make some progress in resolving a conjecture of Boyd and Carr about the ratio of an optimal 2-matching to the subtour LP bound in the worst case. We also begin a study of the subtour LP bound for the extremely simple case in which all costs $c_{ij}$ are either 1 or 2. For these instances we can show that the subtour LP is always strictly better than 3/4 times the length of an optimal tour.

These results are joint work with Jiawei Qian, Frans Schalekamp, and Anke van Zuylen.

** Colloquium - 16:00**

*Abstract:*

Resource allocation problems play a key role in many applications,
including traffic networks, telecommunication networks and economics. In most applications, the allocation of
resources is determined by a finite
number of players, each optimizing an individual objective function. An
important question in
all these applications is the degree of suboptimality caused by selfish
resource allocation.
Because the set of Nash equilibria of a resource allocation game
depends on the cost sharing methods used,
I will first introduce the fundamental problem of designing a cost
sharing method so as to minimize the worst-case efficiency loss.
Then I will present a generic upper bound on the worst-case efficiency
loss for a class of cost sharing methods used in practice.
The power of this bound is demonstrated by applying it to three well
known cost sharing methods: incremental cost sharing,
marginal cost pricing, and average cost sharing.

Letzte Aktualisierung:
16.05.2011