This course covers the following topics: Convex sets and functions, properties of convex functions, convex programming problems, KKT optimality conditions, quadratic programming problems, separable programming, geometric programming, dynamic programming, search techniques, Fourier series, and numerical integration. The course aims to teach students about non-linear programming and optimization techniques. The intended audience for this course is individuals interested in learning about advanced mathematical optimization methods. The teaching method includes theoretical explanations, problem-solving sessions, and practical applications.
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
