Explore advanced optimization techniques in this comprehensive course on non-linear programming. Delve into convex sets and functions, examining their properties across multiple sessions. Master convex programming problems and Karush-Kuhn-Tucker (KKT) optimality conditions. Gain expertise in quadratic and separable programming, followed by an in-depth study of geometric programming over three sessions. Dive into dynamic programming, including its application in finding the shortest path in networks. Learn various search techniques and explore Fourier Series. Conclude with numerical integration methods, focusing on Simpson's Rule.