Lecture 1 - Linear Programming, an Example

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Lecture 2 - Introduction to Linear Programming

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Lecture 3 - Gaussian Elimination with Examples

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Lecture 4 - Summary of Gaussian Elimination

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Lecture 5 - Vector Space over real numbers

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Lecture 6 - Linear Operators

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Lecture 7 - Solutions of Linear Equations

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Lecture 8 - Resource Allocation as LP

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Lecture 9 - Approximate Degree as LP

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Lecture 10 - Equivalent LP's

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Lecture 11 - Introduction to Convexity

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Lecture 12 - Different Kind of Convex Sets

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Lecture 13 - Feasible Region of LP

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Lecture 14 - Proof of Weyl's Theorem

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Lecture 15 - Definition of Convex Functions

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Lecture 16 - Properties of Convex Functions and Examples

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Lecture 17 - Basic Feasible Solution

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Lecture 18 - BFS and Vertices

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Lecture 19 - Simplex Algorithm

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Lecture 20 - Details of Simplex Algorithm

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Lecture 21 - Starting BFS

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Lecture 22 - Degeneracy

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Lecture 23 - Introduction to Duality

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Lecture 24 - Hyperplane Separation Theorems

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Lecture 25 - Farkas Lemma

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Lecture 26 - How to take dual

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Lecture 27 - Examples of taking dual

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Lecture 28 - Strong Duality

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Lecture 30 - Complementary Slackness

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Lecture 29 - Proof of Strong Duality

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Lecture 30 - Complementary Slackness

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Lecture 31 - Introduction to Algorithmic Game Theory

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Lecture 32 - Nash Equilibrium

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Lecture 33 - Minimax and Nash Equilibrium

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Lecture 34 - Deterministic Communication Complexity

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Lecture 35 - Randomized Communication Complexity

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Lecture 36 - Yao's Minimax Theorem

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Lecture 37 - Lower bounds using Yao's Minimax

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Lecture 38 - Set Disjointness Problem

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Lecture 39 - LP for mass flow problem

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Lecture 40 - LP for min cut problem

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Lecture 41 - Max flow = Min cut

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Lecture 42 - Primal dual approach

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Lecture 43 - Primal dual for max flow

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Lecture 44 - Set cover problem

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Lecture 45 - Rounding for set cover

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Lecture 46 - Analysis of Rounding

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Lecture 47 - Algorithm for Set Cover

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Lecture 48 - Linear Regression through LP

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Lecture 49 - Linear Classifiers through LP

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