Cost Flow

Cost flow problems, encompassing variations like minimum cost flow and optimal transport, aim to find the most efficient way to distribute resources across a network, minimizing cost or maximizing flow. Current research emphasizes developing faster algorithms, particularly for dynamic scenarios where network parameters change, using techniques such as skip orthogonal lists and machine learning-based algorithm selection to improve efficiency and accuracy. These advancements have significant implications for diverse applications, including machine learning, multi-object tracking, and robotic path planning, by enabling more efficient solutions to complex optimization problems.