Explore advanced concepts in online linear programming and learning through this comprehensive lecture from the 2019 ADSI Summer Workshop on Algorithmic Foundations of Learning and Control. Delve into resource allocation, comparative ratios, and dynamic learning as Stanford University's Yinyu Ye presents "Further Developments on Online Linear Programming and Learning." Examine key ideas, impossibility results, and convergence theories while gaining insights into stochastic processes, competitive ratios, and the YG algorithm. Discover practical applications, closed-loop solutions, and simulation results that demonstrate the power of these techniques. Engage with a generic framework and explore the large L regime to enhance your understanding of cutting-edge algorithmic approaches in learning and control systems.