Research Interests:
Research Interests:
Research Interests:
Abstract. We propose a new gradient based scheme to approximate Nash equilibria of large sequential two-player, zero-sum games. The algorithm uses modern smoothing techniques for saddle-point problems tailored specifically for the... more
Abstract. We propose a new gradient based scheme to approximate Nash equilibria of large sequential two-player, zero-sum games. The algorithm uses modern smoothing techniques for saddle-point problems tailored specifically for the polytopes used in the Nash ...
Research Interests:
Research Interests:
We develop rst-order smoothing techniques for saddle-point problems that arise in the Nash equilibria computation of sequential games. The crux of our work is a construction of suitable prox-functions for a certain class of polytopes that... more
We develop rst-order smoothing techniques for saddle-point problems that arise in the Nash equilibria computation of sequential games. The crux of our work is a construction of suitable prox-functions for a certain class of polytopes that encode the sequential nature of the games. An implementation based on our smooth- ing techniques computes approximate Nash equilibria for games that are four
Research Interests:
We develop a heuristic algorithm for scheduling multiple factory cranes.Jobs are assigned to cranes, and crane movements scheduled to avoid interference.Each job consists of two or more tasks executed in sequence by the same crane.We... more
We develop a heuristic algorithm for scheduling multiple factory cranes.Jobs are assigned to cranes, and crane movements scheduled to avoid interference.Each job consists of two or more tasks executed in sequence by the same crane.We obtain provably optimal, or near-optimal solutions on realistic problems.A heuristic algorithm is presented for scheduling the movement of multiple factory cranes mounted on a common track. The cranes must complete a sequence of tasks at locations along the track without crossing paths, while adhering as closely as possible to a factory production schedule. The algorithm creates a decision tree of possible states of the crane system, which evolves over time as tasks are assigned and sequenced. By identifying and removing inferior states from the tree, the algorithm efficiently generates provably optimal or near-optimal crane schedules, depending on the complexity of the problem instance.