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July 2024 - Distributed Optimization and Machine Learning
48 videos • 13,988 views • by NPTEL IIT Bombay
1
Week 12: Lecture 46: Objective Inconsistency Problem
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2
Week 12: Lecture 47: General Update Rule
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3
Week 12: Lecture 45: Sources of Computational Heterogenity in FL
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4
Week 11: Lecture 40: Decentralized Stochastic Gradient Descent
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5
Week 11: Lecture 43: FedAvg Algorithm
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6
Week 11: Lecture 44: Convergence Analysis of FL
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7
Week 11: Lecture 42: Introduction to Federated Learning
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8
Week 11: Lecture 41: Decentralized Stochastic Gradient Descent -2
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9
Week 10: Lecture 35: Algorithms for Distributed Optimization
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10
Week 10: Lecture 36: Algorithms for Distributed Optimization-2
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11
Week 10: Lecture 37: Continuous-time Distributed Optimization Algorithms
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12
Week 10: Lecture 38: Introduction to Neural Networks
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13
Week 10: Lecture 39: Large Scale Machine Learning
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14
Week 9: Lecture 30: Consensus Algorithms
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15
Week 9: Lecture 31: Consensus Algorithms-Fixed time
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16
Week 9: Lecture 32: Distributed Economic Dispatch Problem
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17
Week 9: Lecture 33: Algorithm for Uncapacitated EDP
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18
Week 9: Lecture 34: Capacitated EDP
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19
Week 8: Lecture 27: Basics of Graph Theory-2
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20
Week 8: Lecture 26: Basics of Graph Theory
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21
Week 8: Lecture 28: Consensus and Average Consensus
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22
Week 8: Lecture 29: Consensus and Average Consensus-2
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23
Week 7: Lecture 23: Method of Multipliers
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24
Week 7: Lecture 24: Dual Ascent and Dual Decomposition
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25
Week 7: Lecture 25: ADMM Algorithm
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26
Week 6: Lecture 19: Advanced Results on PL inequality: Part 1
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27
Week 6: Lecture 21: Constrained Optimization Problem
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28
Week 6: Lecture 20: Advanced Results on PL inequality: Part 2
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29
Week 6: Lecture 22: Augmented Lagrangian
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30
Week 5: Lecture 18: Rescaled Gradient Flow
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31
Week 5: Lecture 17: Bregman Divergance
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32
Week 5: Lecture 16: Exponential stability
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33
Week 4: Lecture 14: Stability theory
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34
Week 4: Lecture 15: Connections to optimization problems
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35
Week 4: Lecture 13: Accelerate the convergence even further
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36
Week 4: Lecture 12: Acceleration under strong convexity
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37
Week 3: Lecture 9: Slaters condition
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38
Week 3: Lecture 10: Analysis of gradient descent algorithm
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39
Week 3: Lecture 11: KKT conditions
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40
Week 2: Lecture 7: Implications of strong convexity
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41
Week 2: Lecture 8: Primal and dual optimization problems
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42
Week 2: Lecture 6: Strictly and strongly convex functions
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43
Week 1: Lecture 3: Course Outline
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44
Week 1: Lecture 2: Analyzing optimization algorithms in continuous time domain
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45
Week 1: Lecture 1: Introduction to optimization
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46
Week 1: Lecture 4: Basics of optimization problems
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47
Week 1: Lecture 5: Convex sets and Convex functions
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48
Course Introduction - Distributed Optimization and Machine Learning
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