We explore the performance characteristics of Python and Rust in the context of a tailor-made parallel Branch & Bound algorithm for optimization models. By analyzing execution times, scalability, and system efficiency, we offer insights into how language choice can impact real-world computational performance.
A study of leveraging the power of Machine Learning in Branching Variable Selection (BVS) in a Branch and Bound Algorithm with insights to our training model implementation.
