Optimal decision tree using dynamic programming

Abstract

Several algorithms can often be used to solve a complex problem, such as the SAT problem or the graph coloring problem. Those algorithms differ in terms of speed based on the size or other features of the problem. Some algorithms perform much faster on a small size while others perform noticeably better on a larger instance. The optimization problem in this case is to select the best-performing algorithm based on the problem features, resulting in a much faster overall runtime. This is defined as the algorithm selection problem. Many different approaches have been used to solve this problem, such as constructing optimal decision trees. However, there is little published data on using optimal decision trees for algorithm selection and one study reveals a problem in finding a feasible solution on a large number of problem instances. We provide new insights into solving algorithm selection using a dynamic programming approach. The motivation to use this novel approach is that recent studies suggest it has lower scalability issues compared to the traditional optimal decision tree algorithms, due to several efficient techniques such as caching and frequency counting method. The investigation has shown that compared to the integer programming method, the dynamic programming approach is significantly faster and is able to solve large problem instances.