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See this for the question bank, project template, and possible problems. .
Marks distribution:
| 30 % | Internal Evaluation | Consists of 2 quizzes (15%) and a project report plus viva (15%) |
| 30 % | Mid-term exam | Theory paper |
| 40 % | End-term exam | Theory paper |
| Date | References | |
|---|---|---|
| Lecture 01 | 5th Jan 2026 | Introduction (Ch 1 in [1]) |
| Lecture 02 | 6th Jan 2026 | Examples (Ch 2 in [1]) |
| Lecture 03 | 7th Jan 2026 | Integer Linear Programming (Ch 3.1 in [1]) |
| Lecture 04 | 12th Jan 2026 | Maximum-Weight Matching (Ch 3.2 in [1]) |
| Lecture 05 | 13th Jan 2026 | Minimum Vertex Cover (Ch 3.3, 3.4 in [1]) |
| Lecture 06 | 19th Jan 2026 | Theory of Linear Programming (Ch 4.1 in [1]) |
| Lecture 07 | 20th Jan 2026 | Solving LP, ILP using SciPy. Python file |
| Lecture 08 | 21st Jan 2026 | Basic Feasible Solution (Ch 4.2 in [1]) |
| Lecture 09 | 27th Jan 2026 | Convex Polyhedra (Ch 4.3, 4.4 in [1]) |
| Nr. | Book | Authors |
|---|---|---|
| [1] | Understanding and Using Linear Programming | Jiri Matousek and Bernd Gartner (Online Copy) |
| [2] | Approximation Algorithms | Vijay Vazirani (Online Copy) |