Iterative Computation of Security Strategies of Matrix Games with Growing Action Set

TL;DR

This paper introduces an efficient iterative method based on the shadow vertex simplex algorithm for updating security strategies in matrix games as new actions are added, reducing computational complexity compared to solving new LPs from scratch.

Contribution

It develops a novel iterative shadow vertex method with relaxed assumptions, enabling faster updates of saddle-point strategies in large-scale matrix games with growing action sets.

Findings

The iterative method reduces computational complexity compared to standard shadow vertex approach.

Probability analysis shows when the old optimum remains valid after new constraints.

Simulation results confirm efficiency and effectiveness of the proposed method.

Abstract

This paper studies how to efficiently update the saddle-point strategy, or security strategy of one player in a matrix game when the other player develops new actions in the game. It is well known that the saddle-point strategy of one player can be computed by solving a linear program. Developing a new action will add a new constraint to the existing LP. Therefore, our problem becomes how to solve the new LP with a new constraint efficiently. Considering the potentially huge number of constraints, which corresponds to the large size of the other player's action set, we use shadow vertex simplex method, whose computational complexity is lower than linear with respect to the size of the constraints, as the basis of our iterative algorithm. We first rebuild the main theorems in shadow vertex method with relaxed assumption to make sure such method works well in our model, then analyze the probability that the old optimum remains optimal in the new LP, and finally provides the iterative shadow vertex method whose computational complexity is shown to be strictly less than that of shadow vertex method. The simulation results demonstrates our main results about the probability of re-computing the optimum and the computational complexity of the iterative shadow vertex method.