NOC:Distributed Optimization and Machine Learning

Lecture 1 - Introduction to optimization

Lecture 2 - Analyzing optimization algorithms in continuous time domain

Lecture 3 - Course Outline

Lecture 4 - Basics of optimization problems

Lecture 5 - Convex sets and Convex functions

Lecture 6 - Strictly and strongly convex functions

Lecture 7 - Implications of strong convexity

Lecture 8 - Primal and dual optimization problems

Lecture 9 - Slaters condition

Lecture 10 - Analysis of gradient descent algorithm

Lecture 11 - KKT conditions

Lecture 12 - Acceleration under strong convexity

Lecture 13 - Accelerate the convergence even further

Lecture 14 - Stability theory

Lecture 15 - Connections to optimization problems

Lecture 16 - Exponential stability

Lecture 17 - Bregman Divergance

Lecture 18 - Rescaled Gradient Flow

Lecture 19 - Advanced Results on PL inequality - Part 1

Lecture 20 - Advanced Results on PL inequality - Part 2

Lecture 21 - Constrained Optimization Problem

Lecture 22 - Augmented Lagrangian

Lecture 23 - Method of Multipliers

Lecture 24 - Dual Ascent and Dual Decomposition

Lecture 25 - ADMM Algorithm

Lecture 26 - Basics of Graph Theory - 1

Lecture 27 - Basics of Graph Theory - 2

Lecture 28 - Consensus and Average Consensus - 1

Lecture 29 - Consensus and Average Consensus - 2

Lecture 30 - Consensus Algorithms

Lecture 31 - Consensus Algorithms - Fixed time

Lecture 32 - Distributed Economic Dispatch Problem

Lecture 33 - Algorithm for Uncapacitated EDP

Lecture 34 - Capacitated EDP

Lecture 35 - Algorithms for Distributed Optimization - 1

Lecture 36 - Algorithms for Distributed Optimization - 2

Lecture 37 - Continuous-time Distributed Optimization Algorithms

Lecture 38 - Introduction to Neural Networks

Lecture 39 - Large Scale Machine Learning

Lecture 40 - Decentralized Stochastic Gradient Descent - 1

Lecture 41 - Decentralized Stochastic Gradient Descent - 2

Lecture 42 - Introduction to Federated Learning

Lecture 43 - FedAvg Algorithm

Lecture 44 - Convergence Analysis of FL

Lecture 45 - Sources of Computational Heterogenity in FL

Lecture 46 - Objective Inconsistency Problem

Lecture 47 - General Update Rule