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.
Overview
Syllabus
Convex sets and Functions.
Properties of Convex functions - I.
Properties of Convex functions - II.
Properties of Convex functions - III.
Convex Programming Problems.
KKT Optimality conditions.
Quadratic Programming Problems - I.
Quadratic Programming Problems - II.
Separable Programming - I.
Separable Programming - II.
Geometric programming I.
Geometric programming II.
Geometric programming III.
Dynamic programming I.
Dynamic programming II.
Dynamic programming approach to find shortest path in any network (Dynamic Programming III).
Dynamic programming IV.
Search Techniques - I.
Search Techniques - II.
Fourier Series.
Numerical Integration - II (Simpson's Rule).
Taught by
Ch 30 NIOS: Gyanamrit
