Parallel Algorithm

Parallel algorithms aim to accelerate computation by distributing tasks across multiple processors, addressing the limitations of sequential approaches for large-scale problems. Current research focuses on improving the efficiency and approximation guarantees of parallel algorithms for diverse applications, including optimization (e.g., submodular maximization, convex optimization), graph problems (e.g., partitioning, min-cut/max-flow), and search algorithms (e.g., A* variants). These advancements are crucial for tackling computationally intensive tasks in various fields, such as machine learning, robotics, and computer vision, enabling faster and more efficient solutions to complex problems.