The field of optimization and clustering is witnessing significant advancements with the development of efficient algorithms for various problems. Researchers are focusing on improving the performance of existing methods, such as hyperparameter optimization and combinatorial optimization, by introducing novel techniques and data structures. For instance, the use of tree-structured Parzen estimators and consistent hashing schemes is enabling faster and more accurate solutions for complex optimization problems. Additionally, the development of dynamic algorithms for clustering problems, such as the Euclidean k-means problem, is providing almost optimal guarantees across multiple parameters. Noteworthy papers in this area include the proposal of a Merge Kernel for Bayesian optimization on permutation space, which achieves a lower computational complexity than the state-of-the-art Mallows kernel, and the introduction of List Offset Merge Sorters, which can merge multiple sorted input lists into a single sorted output list in a fast and efficient manner.