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Introduction - Applied Optimization for Wireless- Prof Aditya Jagannatham
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Lec 01 | Applied Optimization | Properties of Vectors and Matrices | IIT Kanpur
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Lec 02 | Applied Optimization | Eigenvectors and Eigenvalues | IIT Kanpur
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Lec 03 | Applied Optimization | Positive Semidefinite (PSD) Matrices | IIT Kanpur
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Lec 04 | Applied Optimization | Inner Product Space and its Properties-I | IIT Kanpur
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Lec 05 | Applied Optimization | Inner Product Space and its Properties -II | IIT Kanpur
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Lec 06 | Applied Optimization | Properties of Norm, Echelon form of a Matrix | IIT Kanpur
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Lec 07 | Applied Optimization | Gram Schmidt Orthogonalization | IIT Kanpur
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Lec 08 | Applied Optimization | Null Space, Trace of a Matrix | IIT Kanpur
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Lec 09 | Applied Optimization | Eigenvalue Decomposition (EVD) | IIT Kanpur
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Lec 10 | Applied Optimization | Matrix Inversion Lemma(Woodbury identity) | IIT Kanpur
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Lec 11 | Applied Optimization | Convex Sets and its Properties | IIT Kanpur
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Lec 12 | Applied Optimization | Examples of Affine set | IIT Kanpur
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Lec 13 | Applied Optimization | Norm Ball and its Application | IIT Kanpur
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Lec 14 | Applied Optimization | Ellipsoid and its Application | IIT Kanpur
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Lec 15 | Applied Optimization | Norm Cone, Polyhedron and its Application | IIT Kanpur
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Lec 16 | Applied Optimization | Cooperative Cellular Transmission | IIT Kanpur
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Lec 17 | Applied Optimization | Positive semidefinite (PSD) Cone | IIT Kanpur
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Lec 18 | Applied Optimization | Affine functions and , l2 , lp , l1 norm balls | IIT Kanpur
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Lec 19 | Applied Optimization | l∞, l0 norm balls and Matrix propertie | IIT Kanpur
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Lec 20 | Applied Optimization | Example problems - I | IIT Kanpur
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Lec 21 | Applied Optimization | Example problems - II | IIT Kanpur
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Lec 22 | Applied Optimization | Example problems - III | IIT Kanpur
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Lec 23 | Applied Optimization | Convex and Concave Functions | IIT Kanpur
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Lec 24 | Applied Optimization | Convex Functions: Properties and examples | IIT Kanpur
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Lec 25 | Applied Optimization | Test for Convexity | IIT Kanpur
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Lec 26 | Applied Optimization | MIMO Receiver Design (LS problem) | IIT Kanpur
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Lec 27 | Applied Optimization | Jensen's Inequality and its Application-I | IIT Kanpur
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Lec 28 | Applied Optimization | Jensen's Inequality and its Application-II | IIT Kanpur
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Lec 29 | Applied Optimization | Operations that preserve Convexity | IIT Kanpur
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Lec 30 | Applied Optimization | Conjugate Function , Test for Convexity:Examples | IIT Kanpur
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Lec 31 | Applied Optimization | Operations preserving Convexity: Examples | IIT Kanpur
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Lec 32 | Applied Optimization | Test for Convexity, Quasi-Convexity: Examples | IIT Kanpur
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Lec 33 | Applied Optimization | Examples on Convex functions| IIT Kanpur
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Lec 34 | Applied Optimization | Beamforming in Multi-antenna Wireless Communication | IIT Kanpur
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Lec 35 | Applied Optimization | Maximal Ratio Combiner for Wireless Systems | IIT Kanpur
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Lec 36 | Applied Optimization | Multi-antenna Beamforming with Interfering User | IIT Kanpur
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Lec 37 | Applied Optimization | Zero-Forcing (ZF) Beamforming with Interfering User | IIT Kanpur
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noc18-ee31-Lecture 38-Practical Application
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noc18-ee31-Lecture 39-Practical Application
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noc18-ee31-Lecture 40- Practical Application
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noc18-ee31-Lec 41 | Applied Optimization | Least Squares problem | IIT Kanpur
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noc18-ee31-Lec 42 | Applied Optimization | Geometric Intuition forLeast Squares | IIT Kanpur
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noc18-ee31-Lec 43 | Applied Optimization | Multi Antenna Channel Estimation | IIT Kanpur
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noc18-ee31-Lec 44 | Applied Optimization | Image Deblurring | IIT Kanpur
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noc18-ee31-Lec 45 | Applied Optimization | Least Norm Signal Estimation | IIT Kanpur
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noc18-ee31-Lec 46 | Applied Optimization | Regularization | IIT Kanpur
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noc18-ee31-Lec 47 | Applied Optimization | Convex Optimization Problem: Representations | IIT Kanpur
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noc18-ee31-Lec 49 - Applied Optimization | Stochastic Linear Program, Gaussian Uncertainty
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noc18-ee31-Lec 48 | Applied Optimization | Linear Program and its Application | IIT Kanpur
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noc18-ee31-Lec 50 -Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -I
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noc18-ee31-Lec 51- Applied Optimization | Multiple Input Multiple Output(MIMO) Beamforming -II
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noc18-ee31-Lec 52 -Applied Optimization | Co-operative Communication -I
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noc18-ee31-Lec 53 -Applied Optimization | Co-operative Communication -II
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noc18-ee31-Lec 54 -Applied Optimization | Co-operative Communication -III
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noc18-ee31-Lec 55 -Applied Optimization | Compressive Sensing -I
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noc18-ee31-Lec 56 | Applied Optimization | Compressive Sensing -II
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noc18-ee31-Lec 57 | Applied Optimization | Orthogonal Matching Pursuit (OMP) algorithm
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noc18-ee31-Lec 58 | Applied Optimization | Example problem on OMP algorithm
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noc18-ee31-Lec 59 | Applied Optimization | Compressive Sensing via L1 norm minimization
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noc18-ee31-Lec 60 | Applied Optimization | Linear Classification Problem-I
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noc18-ee31-Lec 61 | Applied Optimization | Linear Classification Problem-II
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noc18-ee31 Lecture 62-Practical Application: Approximate Classifier Design
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noc18-ee31 Lecture 63-Concept of Duality
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noc18-ee31 Lecture 64-Relation between optimal value of Primal & Dual Problems
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noc18-ee31 Lecture 65-Example problem on Strong Duality
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noc18-ee31 Lecture 66-Karush-Kuhn-Tucker(KKT) condition
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noc18-ee31 Lecture 67-Application of KKT condition:Optimal MIMO power allocation(Waterfilling)
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noc18-ee31 lec 68-Optimal MIMO Power allocation(Waterfilling)-II
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noc18-ee31 lec 69-Example problem on Optimal MIMO Power allocation(Waterfilling))
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noc18-ee31 lec 70-Examples : Linear objective with box constraints, Linear Programming
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noc18-ee31 lec 71-Examples:/1 minimization with /x norm constraints , Network Flow problem
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noc18-ee31 lec 72-Examples on Quadratic Optimization
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noc18-ee31 lec 73-Examples on Duality: Dual Norm, Dual of Linear Program(LP)
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noc18-ee31 Lecture 74-Examples on Duality: Min-Max problem, Analytic Centering
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noc18-ee31 Lecture 75-semi Definite Program(SDP) and its application:MIMO symbol vector decoding
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noc18-ee31 Lecture 76-Application:SDP for MIMO Maximum Likelihood(ML) Detection
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noc18-ee31 Lecture 77-Introduction to big Data: Online Recommender System(Netflix)
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noc18-ee31 Lecture 78-matrix Completion Problem in Big Data: Netflix-I
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noc18-ee31 Lecture 79-Matrix Completion Problem in Big Data: Netflix-II
Description:
COURSE OUTLINE: This course is focused on developing the fundamental tools/ techniques in modern optimization as well as illustrating their applications in diverse fields such as Wireless Communication, Signal Processing, Machine Learning, Big Data and Finance. ABOUT INSTRUCTOR: Prof. Aditya K. Jagannatham received his Bachelors degree from the Indian Institute of Technology, Bombay and M.S. and Ph.D. degrees from the University of California, San Diego, U.S.A.. From April 07 to May 09 he was employed as a senior wireless systems engineer at Qualcomm Inc., San Diego, California, where he worked on developing 3G UMTS/WCDMA/HSDPA mobile chipsets as part of the Qualcomm CDMA technologies division.

Applied Optimization for Wireless, Machine Learning, Big Data

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Introduction - Applied Optimization for Wireless- Prof Aditya Jagannatham