In this paper we consider the problem of finding a global optimum of a multimodal function applying path relinking. In particular, we target unconstrained large scale problems and compare two variants of this methodology: the static and the evolutionary path relinking. Both are based on the strategy of creating trajectories of moves passing through high quality solutions in order to incorporate...

Background: Advertisers optimization is one of the most fundamental tasks in paid search, which is a multi-billion industry as a major part of the growing online advertising market. As paid search is a three-player game (advertisers, search users and publishers), how to optimize large-scale advertisers to achieve their expected performance becomes a new challenge, for which adaptive models have...

A pseudo-optimal solution to the poker variant, Two-Player Limit Texas Hold’em was developed and tested against existing world-class poker algorithms. Techniques used in creating the pseudo-optimal solution were able to simplify the problem from complexity from O(10^18) to O(10^7). To achieve this reduction, bucketing/grouping techniques were employed, as were methods replacing the chance nodes...

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newt...