The recent developments in the field of optimization and search algorithms have been marked by significant advancements in efficiency, scalability, and the integration of machine learning techniques. A notable trend is the enhancement of traditional algorithms through the incorporation of reinforcement learning and machine learning models, aiming to solve large-scale problems more effectively. This includes the development of novel algorithms that not only improve computational speed but also ensure the quality of solutions, addressing the challenges of scalability and complexity in optimization problems.

Another key direction is the focus on parallelization and external memory techniques to tackle large-scale problems, with a particular emphasis on bidirectional search algorithms. These advancements have demonstrated superior performance over traditional unidirectional algorithms, even when equipped with state-of-the-art heuristics, marking a significant milestone in the field.

Moreover, the application of machine learning to model reduction in mixed-integer linear programming (MILP) has shown promising results, offering a significant speedup over commercial solvers. This approach leverages the correlation between problem structure and solution, enabling faster and more interpretable solutions to complex MILP problems.

In the realm of online prediction and approximation, innovative policies have been proposed to address the challenges of online sparse linear approximation, offering efficient solutions with theoretical guarantees on performance. Additionally, the integration of reinforcement learning with heuristic methods has been explored to solve hierarchical directed arc routing problems, demonstrating the potential of hybrid algorithms to adapt dynamically and improve solution quality.

Noteworthy Papers:

• Fast Family Column Generation (FFCG): Introduces a reinforcement-learning-based approach to column generation, significantly reducing iterations and computing time for large-scale linear programs.
• Parallel External-Memory Bidirectional Search: Presents a framework that integrates uni- and bi-directional best-first search algorithms, showcasing the superiority of bidirectional search in large-scale problems.
• Fast and Interpretable Mixed-Integer Linear Program Solving: Proposes a preference-based model reduction learning method, achieving significant speedups over commercial solvers.
• Follow The Sparse Approximate Leader: Offers an efficient online meta-policy for sparse linear approximation with theoretical performance guarantees.
• Hybridising Reinforcement Learning and Heuristics: Develops a hybrid algorithm for hierarchical directed arc routing problems, improving speed without compromising solution quality.
• Diversity Optimization for Travelling Salesman Problem: Introduces a deep reinforcement learning based neural solver for discovering diverse yet high-quality solutions to the TSP.