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Numerical Optimization. Instructor: Prof. Shirish K. Shevade, Department of Computer Science and Automation, IISc Bangalore. This course is about studying optimization algorithms, and their applications in different fields.
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Description
Mathematical Background: Convex sets and functions, Need for constrained methods in solving constrained problems.
Unconstrained optimization: Optimality conditions, Line Search Methods, Quasi-Newton Methods, Trust Region Methods, Conjugate Gradient Methods, Least Squares Problems.
Constrained Optimization: Optimality Conditions and Duality, Convex Programming Problem, Linear Programming Problem, Quadratic Programming, Dual Methods, Penalty and Barrier Methods, Interior Point Methods. (from nptel.ac.in)
Course content
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- Lecture 01 – Introduction Unlimited
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- Lecture 02 – Mathematical Background Unlimited
- Lecture 03 – Mathematical Background (cont.) Unlimited
- Lecture 04 – One Dimensional Optimization – Optimality Conditions Unlimited
- Lecture 05 – One Dimensional Optimization (cont.) Unlimited
- Lecture 08 – Convex Functions Unlimited
- Lecture 09 – Convex Functions (cont.) Unlimited
- Lecture 20 – Constrained Optimization – Local and Global Solutions, Conceptual Algorithm Unlimited
- Lecture 21 – Feasible and Descent Directions Unlimited
- Lecture 22 – First Order KKT Conditions Unlimited
- Lecture 23 – Constraint Qualifications Unlimited
- Lecture 24 – Convex Programming Problem Unlimited
- Lecture 25 – Second Order KKT Conditions Unlimited
- Lecture 26 – Second Order KKT Conditions (cont.) Unlimited
- Lecture 30 – Linear Programming Problem Unlimited
- Lecture 31 – Geometric Solution Unlimited
- Lecture 32 – Basic Feasible Solution Unlimited
- Lecture 33 – Optimality Conditions and Simplex Tableau Unlimited
- Lecture 34 – Simplex Algorithm and Two-Phase Method Unlimited
- Lecture 35 – Duality in Linear Programming Unlimited
- Lecture 36 – Interior Point Methods – Affine Scaling Method Unlimited
- Lecture 37 – Karmakar’s Method Unlimited
Instructor
