Learn · Distributed Optimization and Machine Learning
Lecture 3 of 47Week 1.1: Introduction to Distributed Optimization
Lecture 3: Course Outline
Prof. Mayank Baranwal · IIT Bombay
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All the lectures
- 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
- Lecture 27: Basics of Graph Theory-2
- Lecture 28: Consensus and Average Consensus
- 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
- 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
- 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