4 papers
Computing all Nash equilibria of low-rank bi-matrix games
Zachary Feinstein, Andreas Löhne, Birgit Rudloff
We study constrained bi-matrix games, with a particular focus on low-rank games. Our main contribution is a framework that reduces low-rank games to smaller, equivalent constrained…
Approximations of unbounded convex projections and unbounded convex sets
Gabriela KováÄová, Birgit Rudloff
We consider the problem of projecting a convex set onto a subspace, or equivalently formulated, the problem of computing a set obtained by applying a linear mapping to a convex fea…
Deep Learning the Efficient Frontier of Convex Vector Optimization Problems
Zachary Feinstein, Birgit Rudloff
In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater's condition. The…
Approximating the set of Nash equilibria for convex games
Zachary Feinstein, Niklas Hey, Birgit Rudloff
In Feinstein and Rudloff (2023), it was shown that the set of Nash equilibria for any non-cooperative player game coincides with the set of Pareto optimal points of a certain v…