MT3444: Combinatorial Optimization


If there is any feedback, please provide it anonymously in this Google form.

Please join this Google classroom for class related announcements.

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


Lectures

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])
Lecture 10 28th Jan 2026 Simplex Method (Ch 5.1 to 5.5 in [1])
Lecture 11 2nd Feb 2026 Simplex Method in General (Ch 5.6, 5.7 in [1])
Lecture 12 3rd Feb 2026 Avoiding Cycle in Simplex (Ch 5.8 in [1], Example )
Quiz-01 4th Feb 2026 Question Paper
Lecture 13 11th Feb 2026 Duality of LP (Ch 6.1 in [1])
Lecture 14 14th Feb 2026 Strong Duality Theorem (Ch 6.3 in [1])
Lecture 15 14th Feb 2026 Farkas Lemma and proof of Duality (Ch 6.4 in [1])
Lecture 16 15th Feb 2026 Proof of Farkas Lemma assuming Lemma 6.5.1 (Ch 6.5 in [1])
Lecture 17 16th Feb 2026 Konig's Theorem by Duality (Ch 8.2 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)