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Randomized Optimization Techniques

Track :

Mathematics

Lessons no : 41

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What will you learn in this course?
  • Master how to implement randomized algorithms for complex optimization problems using practical techniques and real-world applications in Hindi
  • Apply probabilistic methods to improve optimization efficiency and solution quality in diverse industries and scenarios
  • Analyze the strengths and limitations of randomized optimization techniques for various problem types and constraints
  • Design and develop customized randomized algorithms to solve specific optimization challenges effectively
  • Evaluate the performance of randomized optimization methods through empirical testing and statistical analysis
  • Utilize advanced tools and software to simulate and optimize real-world problems with randomized algorithms
  • Integrate randomized techniques into existing optimization workflows to enhance problem-solving capabilities
  • Identify suitable randomized algorithms for large-scale, high-dimensional, and stochastic optimization problems

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Lessons | 41
Randomized Algorithms Intro Video
00:05:37

Randomized Algorithms Intro Video
Lecture 1 Introduction to Randomized Algorithms
00:44:09

Lecture 1 Introduction to Randomized Algorithms
Lecture 2 Randomized Mincut Algorithm
00:42:26

Lecture 2 Randomized Mincut Algorithm
Lecture 3 Randomized Find
00:20:20

Lecture 3 Randomized Find
Lecture 4 Probability Review
00:29:09

Lecture 4 Probability Review
Lecture 5 Expectation of Random Variables
00:45:05

Lecture 5 Expectation of Random Variables
Lec 6 Conditional Probability and Conditional Expectation2
00:31:50

Lec 6 Conditional Probability and Conditional Expectation2
Lec 7 Birthday Paradox
00:15:51

Lec 7 Birthday Paradox
Lecture 8 Markov and Chebychev s Inequalities
00:28:26

Lecture 8 Markov and Chebychev s Inequalities
Lecture 9 Median Algorithm
00:58:40

Lecture 9 Median Algorithm
Lecture 10 Chernoff Bound
00:48:09

Lecture 10 Chernoff Bound
Lec 11 Permutation Routing on a Hypercube
00:32:35

Lec 11 Permutation Routing on a Hypercube
Lec 12 Permutation Routing on a Hypercube Analysis
00:26:24

Lec 12 Permutation Routing on a Hypercube Analysis
Lec 13 Introduction to Probabilistic Method
00:38:19

Lec 13 Introduction to Probabilistic Method
Lec 14 More Examples on Probabilistic Method
00:49:10

Lec 14 More Examples on Probabilistic Method
Lec 15 Lovasz Local Lemma
00:36:38

Lec 15 Lovasz Local Lemma
Lec 16 Introduction to Markov Chains
00:45:12

Lec 16 Introduction to Markov Chains
Lec 17 2 SAT and Markov Chains
01:07:12

Lec 17 2 SAT and Markov Chains
Lec 18 3 SAT and Markov Chains
00:59:39

Lec 18 3 SAT and Markov Chains
Lec 19 Electrical Networks
00:34:51

Lec 19 Electrical Networks
Lec 20 Cover Time
00:24:45

Lec 20 Cover Time
Lec 21 Rapid Mixing
00:46:11

Lec 21 Rapid Mixing
Lec 22 Introduction to Computational Complexity
00:38:52

Lec 22 Introduction to Computational Complexity
Lec 23 Pratt s Certificate
00:16:11

Lec 23 Pratt s Certificate
Lec 24 Primality Testing
00:50:52

Lec 24 Primality Testing
Lec 25 Miller Rabin Algorithm
00:39:55

Lec 25 Miller Rabin Algorithm
Lec 26 All pair shortest path I
00:26:12

Lec 26 All pair shortest path I
Lec 27 All pair shortest path II
00:54:08

Lec 27 All pair shortest path II
Lec 28 Randomized MST
00:53:24

Lec 28 Randomized MST
Lec 29 Introduction to approximate counting
00:29:14

Lec 29 Introduction to approximate counting
Lec 30 DNF counting
00:43:23

Lec 30 DNF counting
Lec 31 Perfect Matching I
00:43:19

Lec 31 Perfect Matching I
Lec 32 Perfect Matching II
00:32:49

Lec 32 Perfect Matching II
Lec 33 Perfect Matching III
00:25:11

Lec 33 Perfect Matching III
Lec 34 Treaps
00:47:58

Lec 34 Treaps
Lec 35 Hashing
00:43:57

Lec 35 Hashing
Lec 36 Probabilistically checkable proofs I
00:44:59

Lec 36 Probabilistically checkable proofs I
Lec 37 Probabilistically checkable proofs II
00:29:59

Lec 37 Probabilistically checkable proofs II
Lec 38 Probabilistically checkable proofs III
00:40:36

Lec 38 Probabilistically checkable proofs III
Lec 39 LFKN Protocol
00:30:23

Lec 39 LFKN Protocol
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Randomized Optimization course in hindi, in this course we will learn about randomized algorithms for solving optimization problems efficiently and effectively.