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Applied Optimization for Wireless, Machine Learning, Big Data

Applied Optimization for Wireless, Machine Learning, Big Data

IIT Kanpur July 2018 via YouTube Direct link

Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur

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Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur

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Applied Optimization for Wireless, Machine Learning, Big Data

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  1. 1 Introduction - Applied Optimization for Wireless- Prof Aditya Jagannatham
  2. 2 Lec 01 | Applied Optimization | Properties of Vectors and Matrices | IIT Kanpur
  3. 3 Lec 02 | Applied Optimization | Eigenvectors and Eigenvalues | IIT Kanpur
  4. 4 Lec 03 | Applied Optimization | Positive Semidefinite (PSD) Matrices | IIT Kanpur
  5. 5 Lec 04 | Applied Optimization | Inner Product Space and its Properties-I | IIT Kanpur
  6. 6 Lec 05 | Applied Optimization | Inner Product Space and its Properties -II | IIT Kanpur
  7. 7 Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur
  8. 8 Lec 07 | Applied Optimization | Gram Schmidt Orthogonalization | IIT Kanpur
  9. 9 Lec 08 | Applied Optimization | Null Space, Trace of a Matrix | IIT Kanpur
  10. 10 Lec 09 | Applied Optimization | Eigenvalue Decomposition (EVD) | IIT Kanpur
  11. 11 Lec 10 | Applied Optimization | Matrix Inversion Lemma(Woodbury identity) | IIT Kanpur
  12. 12 Lec 11 | Applied Optimization | Convex Sets and its Properties | IIT Kanpur
  13. 13 Lec 12 | Applied Optimization | Examples of Affine set | IIT Kanpur
  14. 14 Lec 13 | Applied Optimization | Norm Ball and its Application | IIT Kanpur
  15. 15 Lec 14 | Applied Optimization | Ellipsoid and its Application | IIT Kanpur
  16. 16 Lec 15 | Applied Optimization | Norm Cone, Polyhedron and its Application | IIT Kanpur
  17. 17 Lec 16 | Applied Optimization | Cooperative Cellular Transmission | IIT Kanpur
  18. 18 Lec 17 | Applied Optimization | Positive semidefinite (PSD) Cone | IIT Kanpur
  19. 19 Lec 18 | Applied Optimization | Affine functions and , l2 , lp , l1 norm balls | IIT Kanpur
  20. 20 Lec 19 | Applied Optimization | l∞, l0 norm balls and Matrix propertie | IIT Kanpur
  21. 21 Lec 20 | Applied Optimization | Example problems - I | IIT Kanpur
  22. 22 Lec 21 | Applied Optimization | Example problems - II | IIT Kanpur
  23. 23 Lec 22 | Applied Optimization | Example problems - III | IIT Kanpur
  24. 24 Lec 23 | Applied Optimization | Convex and Concave Functions | IIT Kanpur
  25. 25 Lec 24 | Applied Optimization | Convex Functions: Properties and examples | IIT Kanpur
  26. 26 Lec 25 | Applied Optimization | Test for Convexity | IIT Kanpur
  27. 27 Lec 26 | Applied Optimization | MIMO Receiver Design (LS problem) | IIT Kanpur
  28. 28 Lec 27 | Applied Optimization | Jensen's Inequality and its Application-I | IIT Kanpur
  29. 29 Lec 28 | Applied Optimization | Jensen's Inequality and its Application-II | IIT Kanpur
  30. 30 Lec 29 | Applied Optimization | Operations that preserve Convexity | IIT Kanpur
  31. 31 Lec 30 | Applied Optimization | Conjugate Function , Test for Convexity:Examples | IIT Kanpur
  32. 32 Lec 31 | Applied Optimization | Operations preserving Convexity: Examples | IIT Kanpur
  33. 33 Lec 32 | Applied Optimization | Test for Convexity, Quasi-Convexity: Examples | IIT Kanpur
  34. 34 Lec 33 | Applied Optimization | Examples on Convex functions| IIT Kanpur
  35. 35 Lec 34 | Applied Optimization | Beamforming in Multi-antenna Wireless Communication | IIT Kanpur
  36. 36 Lec 35 | Applied Optimization | Maximal Ratio Combiner for Wireless Systems | IIT Kanpur
  37. 37 Lec 36 | Applied Optimization | Multi-antenna Beamforming with Interfering User | IIT Kanpur
  38. 38 Lec 37 | Applied Optimization | Zero-Forcing (ZF) Beamforming with Interfering User | IIT Kanpur
  39. 39 noc18-ee31-Lecture 38-Practical Application
  40. 40 noc18-ee31-Lecture 39-Practical Application
  41. 41 noc18-ee31-Lecture 40- Practical Application
  42. 42 noc18-ee31-Lec 41 | Applied Optimization | Least Squares problem | IIT Kanpur
  43. 43 noc18-ee31-Lec 42 | Applied Optimization | Geometric Intuition forLeast Squares | IIT Kanpur
  44. 44 noc18-ee31-Lec 43 | Applied Optimization | Multi Antenna Channel Estimation | IIT Kanpur
  45. 45 noc18-ee31-Lec 44 | Applied Optimization | Image Deblurring | IIT Kanpur
  46. 46 noc18-ee31-Lec 45 | Applied Optimization | Least Norm Signal Estimation | IIT Kanpur
  47. 47 noc18-ee31-Lec 46 | Applied Optimization | Regularization | IIT Kanpur
  48. 48 noc18-ee31-Lec 47 | Applied Optimization | Convex Optimization Problem: Representations | IIT Kanpur
  49. 49 noc18-ee31-Lec 49 - Applied Optimization | Stochastic Linear Program, Gaussian Uncertainty
  50. 50 noc18-ee31-Lec 48 | Applied Optimization | Linear Program and its Application | IIT Kanpur
  51. 51 noc18-ee31-Lec 50 -Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -I
  52. 52 noc18-ee31-Lec 51- Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -II
  53. 53 noc18-ee31-Lec 52 -Applied Optimization | Co-operative Communication -I
  54. 54 noc18-ee31-Lec 53 -Applied Optimization | Co-operative Communication -II
  55. 55 noc18-ee31-Lec 54 -Applied Optimization | Co-operative Communication -III
  56. 56 noc18-ee31-Lec 55 -Applied Optimization | Compressive Sensing -I
  57. 57 noc18-ee31-Lec 56 | Applied Optimization | Compressive Sensing -II
  58. 58 noc18-ee31-Lec 57 | Applied Optimization | Orthogonal Matching Pursuit (OMP) algorithm
  59. 59 noc18-ee31-Lec 58 | Applied Optimization | Example problem on OMP algorithm
  60. 60 noc18-ee31-Lec 59 | Applied Optimization | Compressive Sensing via L1 norm minimization
  61. 61 noc18-ee31-Lec 60 | Applied Optimization | Linear Classification Problem-I
  62. 62 noc18-ee31-Lec 61 | Applied Optimization | Linear Classification Problem-II
  63. 63 noc18-ee31 Lecture 62-Practical Application: Approximate Classifier Design
  64. 64 noc18-ee31 Lecture 63-Concept of Duality
  65. 65 noc18-ee31 Lecture 64-Relation between optimal value of Primal & Dual Problems
  66. 66 noc18-ee31 Lecture 65-Example problem on Strong Duality
  67. 67 noc18-ee31 Lecture 66-Karush-Kuhn-Tucker(KKT) condition
  68. 68 noc18-ee31 Lecture 67-Application of KKT condition:Optimal MIMO power allocation(Waterfilling)
  69. 69 noc18-ee31 lec 68-Optimal MIMO Power allocation(Waterfilling)-II
  70. 70 noc18-ee31 lec 69-Example problem on Optimal MIMO Power allocation(Waterfilling))
  71. 71 noc18-ee31 lec 70-Examples : Linear objective with box constraints, Linear Programming
  72. 72 noc18-ee31 lec 71-Examples:/1 minimization with /x norm constraints , Network Flow problem
  73. 73 noc18-ee31 lec 72-Examples on Quadratic Optimization
  74. 74 noc18-ee31 lec 73-Examples on Duality: Dual Norm, Dual of Linear Program(LP)
  75. 75 noc18-ee31 Lecture 74-Examples on Duality: Min-Max problem, Analytic Centering
  76. 76 noc18-ee31 Lecture 75-semi Definite Program(SDP) and its application:MIMO symbol vector decoding
  77. 77 noc18-ee31 Lecture 76-Application:SDP for MIMO Maximum Likelihood(ML) Detection
  78. 78 noc18-ee31 Lecture 77-Introduction to big Data: Online Recommender System(Netflix)
  79. 79 noc18-ee31 Lecture 78-matrix Completion Problem in Big Data: Netflix-I
  80. 80 noc18-ee31 Lecture 79-Matrix Completion Problem in Big Data: Netflix-II

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