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