Symmetric Groups (Abstract Algebra)
Symmetric groups are some of the most essential types of finite groups. A symmetric group is the group of permutations on a set. The group of permutations on a set of n-elements is denoted S_n. Symmetric groups capture the history of abstract algebra, provide a wide range of examples in
From playlist Abstract Algebra
Symmetric matrices - eigenvalues & eigenvectors
Free ebook http://tinyurl.com/EngMathYT A basic introduction to symmetric matrices and their properties, including eigenvalues and eigenvectors. Several examples are presented to illustrate the ideas. Symmetric matrices enjoy interesting applications to quadratic forms.
From playlist Engineering Mathematics
Comparing the Solutions to Homogeneous and Nonhomogeneous Systems
This video compares the solutions to a homogeneous system and nonhomogeneous system of equations.
From playlist Rank and Homogeneous Systems
What is the Symmetric Difference of 2 Sets?
What is the symmetric difference of 2 sets? In this video we go over the symmetric difference of sets, explaining it in a couple ways including what is probably the briefest way. The symmetric difference of two sets A and B is (A union B)-(A intersect B). If you need to know what the defin
From playlist Set Theory
Definition of Rank and showing Rank(A) = Dim Col(A) In this video, I define the notion of rank of a matrix and I show that it is the same as the dimension of the column space of that matrix. This is another illustration of the beautiful interplay between linear transformations and matrice
From playlist Linear Equations
This video is one of the cornerstones of our dual space extravaganza. Using just some facts about annihilators (in a video below), I provide a very elegant and neat proof of the fact that the rank of A^T equals to the rank of A. Annihilator: https://www.youtube.com/watch?v=ISs13W6474g Cl
From playlist Dual Spaces
Symmetric and skew symmetric matricies (Ch5 Pr15)
Here we show that A+A^T and AA^T are symmetric matrices, and A-A^T is skew symmetric for A is a square matrix. Presented by N J Wildberger of the School of Mathematics and Statistics, Faculty of Science, UNSW.
From playlist Mathematics 1A (Algebra)
Rank, Basic Variables, and Free Variables of a Coefficient Matrix
This video explains how to determine the rank, basic variables, and free variables of a given coefficient matrix.
From playlist Rank and Homogeneous Systems
Nonlinear algebra, Lecture 8: "Tensors", by Bernd Sturmfels and Mateusz Michalek
This is the eight lecture in the IMPRS Ringvorlesung, the advanced graduate course at the Max Planck Institute for Mathematics in the Sciences.
From playlist IMPRS Ringvorlesung - Introduction to Nonlinear Algebra
Anna Seigal: "From Linear Algebra to Multi-Linear Algebra"
Watch part 2/2 here: https://youtu.be/f5MiPayz_e8 Tensor Methods and Emerging Applications to the Physical and Data Sciences Tutorials 2021 "From Linear Algebra to Multi-Linear Algebra" Anna Seigal - University of Oxford Abstract: Linear algebra is the foundation to methods for finding
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
11. Matrix Spaces; Rank 1; Small World Graphs
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 11. Matrix Spaces; Rank 1; Small World Graphs License: Creative Commons BY-NC-SA More informat
From playlist MIT 18.06 Linear Algebra, Spring 2005
Determinantal varieties and asymptotic expansion of Bergman kernels by Harald Upmeier
DISCUSSION MEETING ANALYTIC AND ALGEBRAIC GEOMETRY DATE:19 March 2018 to 24 March 2018 VENUE:Madhava Lecture Hall, ICTS, Bangalore. Complex analytic geometry is a very broad area of mathematics straddling differential geometry, algebraic geometry and analysis. Much of the interactions be
From playlist Analytic and Algebraic Geometry-2018
Shmuel Friedland: "Complexity of Computation of Tensor Rank and Best Rank One Approximation"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop IV: Efficient Tensor Representations for Learning and Computational Complexity "Complexity of Computation of Tensor Rank and Best Rank One Approximation" Shmuel Friedland - University of Illinois at C
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Algebraic Statistics of Random Integral Matrices by Hoi H. Nguyen
PROGRAM: TOPICS IN HIGH DIMENSIONAL PROBABILITY ORGANIZERS: Anirban Basak (ICTS-TIFR, India) and Riddhipratim Basu (ICTS-TIFR, India) DATE & TIME: 02 January 2023 to 13 January 2023 VENUE: Ramanujan Lecture Hall This program will focus on several interconnected themes in modern probab
From playlist TOPICS IN HIGH DIMENSIONAL PROBABILITY
Parallel session 8 by Dave Constantine
Geometry Topology and Dynamics in Negative Curvature URL: https://www.icts.res.in/program/gtdnc DATES: Monday 02 Aug, 2010 - Saturday 07 Aug, 2010 VENUE : Raman Research Institute, Bangalore DESCRIPTION: This is An ICM Satellite Conference. The conference intends to bring together ma
From playlist Geometry Topology and Dynamics in Negative Curvature
LG/CFT seminar - Poisson structures 2
This is a seminar series on the Landau-Ginzburg / Conformal Field Theory correspondence, and various mathematical ingredients related to it. This particular lecture is about Poisson varieties and Poisson manifolds, including the concept of rank. This video was recorded in the pocket Delta
From playlist Landau-Ginzburg seminar
[BOURBAKI 2019] Higher rank Teichmüller theories - Pozzetti - 30/03/19
Beatrice POZZETTI Higher rank Teichmüller theories Let Γ be the fundamental group of a compact surface S with negative Euler characteristic, and G denote PSL(2, R), the group of isometries of the hyperbolic plane. Goldman observed that the Teichmüller space, the parameter space of marked
From playlist BOURBAKI - 2019
Rank, and the Relationship between Col(A) and Null(A)
Description: Associated to every matrix is a number called the rank, defined to be the Dimension of the Column Space (aka the number of leading 1s). We get the wonderful relation that the dimension of Col(A) plus the diemnsion of Null(A) adds to the number of columns n. Learning Objectiv
From playlist Older Linear Algebra Videos
Quantitative Inverse Theorem for Gowers Uniformity Norms 𝖴5 and 𝖴6 in 𝔽n2 - Luka Milicevic
Workshop on Additive Combinatorics and Algebraic Connections Topic: Quantitative Inverse Theorem for Gowers Uniformity Norms 𝖴5 and 𝖴6 in 𝔽n2 Speaker: Luka Milicevic Affiliation: Serbian Academy of Sciences and Arts Date: October 27, 2022 In this talk, I will discuss a proof of a quant
From playlist Mathematics