In linear algebra, a semi-orthogonal matrix is a non-square matrix with real entries where: if the number of columns exceeds the number of rows, then the rows are orthonormal vectors; but if the number of rows exceeds the number of columns, then the columns are orthonormal vectors. Equivalently, a non-square matrix A is semi-orthogonal if either In the following, consider the case where A is an m × n matrix for m > n.Then The fact that implies the isometry property for all x in Rn. For example, is a semi-orthogonal matrix. A semi-orthogonal matrix A is (either A†A = I or AA† = I) and either left-invertible or right-invertible (left-invertible if it has more rows than columns, otherwise right invertible). As a linear transformation applied from the left, a semi-orthogonal matrix with more rows than columns preserves the dot product of vectors, and therefore acts as an isometry of Euclidean space, such as a rotation or reflection. (Wikipedia).
Linear Algebra 7.1 Orthogonal Matrices
My notes are available at http://asherbroberts.com/ (so you can write along with me). Elementary Linear Algebra: Applications Version 12th Edition by Howard Anton, Chris Rorres, and Anton Kaul A. Roberts is supported in part by the grants NSF CAREER 1653602 and NSF DMS 2153803.
From playlist Linear Algebra
Math 060 Fall 2017 111517C Orthonormal Bases, Orthogonal Matrices, and Method of Least Squares
Definition of orthogonal matrices. Example: rotation matrix. Properties: Q orthogonal if and only if its transpose is its inverse. Q orthogonal implies it is an isometry; that it is isogonal (preserves angles). Theorem: How to find, given a vector in an inner product space, the closest
From playlist Course 4: Linear Algebra (Fall 2017)
Orthogonal Matrix Example (Ch5 Pr28)
We look at a rotation matrix as an example of a orthogonal matrix. This is Chapter 5 Problem 28 from the MATH1141/MATH1131 Algebra notes. Presented by Daniel Mansfield from the School of Mathematics and Statistics at UNSW.
From playlist Mathematics 1A (Algebra)
Quaternions as 4x4 Matrices - Connections to Linear Algebra
In math, it's usually possible to view an object or concept from many different (but equivalent) angles. In this video, we will see that the quaternions may be viewed as 4x4 real-valued matrices of a special form. What is interesting here is that if you know how to multiply matrices, you a
From playlist Quaternions
Matrices: Inverse of 2x2 and 3x3 Matrix
This is the sixth video of a series from the Worldwide Center of Mathematics explaining the basics of matrices. This video deals with finding the inverse of a square 2x2 or 3x3 matrix. For more math videos, visit our channel or go to www.centerofmath.org
From playlist Basics: Matrices
Orthogonal matrices | Lecture 7 | Matrix Algebra for Engineers
Definition of orthogonal matrices. Join me on Coursera: https://www.coursera.org/learn/matrix-algebra-engineers Lecture notes at http://www.math.ust.hk/~machas/matrix-algebra-for-engineers.pdf Subscribe to my channel: http://www.youtube.com/user/jchasnov?sub_confirmation=1
From playlist Matrix Algebra for Engineers
This video defines a diagonal matrix and then explains how to determine the inverse of a diagonal matrix (if possible) and how to raise a diagonal matrix to a power. Site: mathispower4u.com Blog: mathispower4u.wordpress.com
From playlist Introduction to Matrices and Matrix Operations
Positive Semi-Definite Matrix 2: Spectral Theorem
Matrix Theory: Following Part 1, we note the recipe for constructing a (Hermitian) PSD matrix and provide a concrete example of the PSD square root. We prove the Spectral Theorem for C^n in the remaining 9 minutes.
From playlist Matrix Theory
Linear Algebra 21j: Two Geometric Interpretations of Orthogonal Matrices
https://bit.ly/PavelPatreon https://lem.ma/LA - Linear Algebra on Lemma http://bit.ly/ITCYTNew - Dr. Grinfeld's Tensor Calculus textbook https://lem.ma/prep - Complete SAT Math Prep
From playlist Part 3 Linear Algebra: Linear Transformations
34. Distance Matrices, Procrustes Problem
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k This lecture conti
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Find an Orthogonal Projection of a Vector Onto a Line Given an Orthogonal Basis (R2)
This video explains how t use the orthogonal projection formula given subset with an orthogonal basis. The distance from the vector to the line is also found.
From playlist Orthogonal and Orthonormal Sets of Vectors
(ML 19.8) Proof that a product of PSD kernels is a PSD kernel
What the title says.
From playlist Machine Learning
MIT 18.06 Linear Algebra, Spring 2005 Instructor: Gilbert Strang View the complete course: http://ocw.mit.edu/18-06S05 YouTube Playlist: https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8 32. Quiz 3 Review License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/t
From playlist MIT 18.06 Linear Algebra, Spring 2005
Quantum chaos, random matrices and statistical physics (Lecture 05) by Arul Lakshminarayan
ORGANIZERS: Abhishek Dhar and Sanjib Sabhapandit DATE: 27 June 2018 to 13 July 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore This advanced level school is the ninth in the series. This is a pedagogical school, aimed at bridging the gap between masters-level courses and topics in
From playlist Bangalore School on Statistical Physics - IX (2018)
Quantum Information Theory by Ashwin Nayak
Dates: Thursday 03 Jan, 2013 - Saturday 05 Jan, 2013 Venue: ICTS-TIFR, IISc Campus, Bangalore The school aims to provide students and researchers an introduction to the field of quantum information, computation and communication. Topics that will be covered include introduction to quantu
From playlist Mini Winter School on Quantum Information and Computation
Extremal Combinatorics with Po-Shen Loh - 04/15 Wed
Carnegie Mellon University is protecting the community from the COVID-19 pandemic by running courses online for the Spring 2020 semester. This is the video stream for Po-Shen Loh’s PhD-level course 21-738 Extremal Combinatorics. Professor Loh will not be able to respond to questions or com
From playlist CMU PhD-Level Course 21-738 Extremal Combinatorics
35. Finding Clusters in Graphs
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018 Instructor: Gilbert Strang View the complete course: https://ocw.mit.edu/18-065S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k The topic of this
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Marcello Bernardara: Semiorthogonal decompositions and birational geometry of geometrically rational
Abstract:This is a joint work in progress with A. Auel. Let S be a geometrically rational del Pezzo surface over a field k. In this talk, I will show how the k-rationality of S is equivalent to the existence of some semiorthogonal decompositions of its derived category. In particular, the
From playlist HIM Lectures: Junior Trimester Program "Algebraic Geometry"
Complex ODEs: Asymptotics, Orthogonal Polynomials and Random Matrices - 18 May 2018
Centro di Ricerca Matematica Ennio De Giorgi http://crm.sns.it/event/429/ Complex ODEs: Asymptotics, Orthogonal Polynomials and Random Matrices An international interdisciplinary workshop, gathering experts in mathematics and mathematical physics, working on the theory of orthogonal and
From playlist Centro di Ricerca Matematica Ennio De Giorgi
The Diagonalization of Matrices
This video explains the process of diagonalization of a matrix.
From playlist The Diagonalization of Matrices