2013/12/03 by Ben Ruijl, Ruijl, Ben, Jos Vermaseren +7 · 2 citations
Computer Science · #Algorithms and Data Compression #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Data Visualization and Analytics #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1312.0841
arxiv created 2013/12/03 · openalex publication_date 2013/12/03 · arxiv updated 2013/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In many applications of computer algebra large expressions must be simplified to make repeated numerical evaluations tractable. Previous works presented heuristically guided improvements, e.g., for Horner schemes. The remaining expression is then further reduced by common subexpression elimination. A recent approach successfully applied a relatively new algorithm, Monte Carlo Tree Search (MCTS) with UCT as the selection criterion, to find better variable orderings. Yet, this approach is fit for further improvements since it is sensitive to the so-called exploration-exploitation constant Cp and the number of tree updates N. In this paper we propose a new selection criterion called Simulated Annealing UCT (SA-UCT) that has a dynamic exploration-exploitation parameter, which decreases with the iteration number i and thus reduces the importance of exploration over time. First, we provide an intuitive explanation in terms of the exploration-exploitation behavior of the algorithm. Then, we test our algorithm on three large expressions of different origins. We observe that SA-UCT widens the interval of good initial values Cp where best results are achieved. The improvement is large (more than a tenfold) and facilitates the selection of an appropriate Cp.