Maximum Network Flow and the Ford-Fulkerson Algorithm

Network optimization is critical for logistics, resource planning, and data routing. If you need to understand how to maximize throughput and identify critical bottlenecks in a complex system, this course provides the analytical framework. This course delivers a deep, text-based understanding of the maximum flow problem and the classic Ford-Fulkerson approach. You will gain the analytical skills necessary to model real-world capacity constraints using graph theory and verify the optimality of your solutions. What you'll learn: * Understand the core terminology of flow networks, including sources, sinks, capacity, and flow constraints. * Master the step-by-step process of the Ford-Fulkerson method for finding augmenting paths. * Apply the fundamental Max-Flow Min-Cut Theorem to verify solutions and identify critical capacity bottlenecks. * Practice analyzing and calculating flow using the concept of residual graphs for efficient pathfinding. * Configure basic graph models to represent real-world logistical and resource allocation problems. The course begins with foundational graph theory and definitions specific to flow networks, progresses through the iterative steps of the algorithm, and concludes with practical written exercises demonstrating the application of the Max-Flow Min-Cut Theorem. This course is designed for beginners interested in algorithms, graph theory, and optimization problems. No prior knowledge of network flow algorithms is required. Start applying powerful optimization techniques today.