In linear algebra, a Hessenberg matrix is a special kind of square matrix, one that is "almost" triangular. To be exact, an upper Hessenberg matrix has zero entries below the first subdiagonal, and a lower Hessenberg matrix has zero entries above the first superdiagonal. They are named after Karl Hessenberg. (Wikipedia).
Paola Boito: Topics in structured linear algebra - lecture 1
CIRM VIRTUAL EVENT Recorded during the meeting "French Computer Algebra Days" the March 01, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audio
From playlist Virtual Conference
2 Construction of a Matrix-YouTube sharing.mov
This video shows you how a matrix is constructed from a set of linear equations. It helps you understand where the various elements in a matrix comes from.
From playlist Linear Algebra
Peter Benner: Matrix Equations and Model Reduction, Lecture 5
Peter Benner from the Max Planck Institute presents: Matrix Equations and Model Reduction; Lecture 5
From playlist Gene Golub SIAM Summer School Videos
Linear Algebra 7.2 Orthogonal Diagonalization
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
Ting Xue: Character sheaves, Hecke algebras and Hessenberg varieties
28 September 2021 Abstract: We discuss character sheaves in the setting of graded Lie algebras. Via a nearby cycle construction irreducible representations of Hecke algebras of complex re ection groups at roots of unity enter the description of character sheaves. Recent work of Lusztig an
From playlist Representation theory's hidden motives (SMRI & Uni of Münster)
12. Computing Eigenvalues and Singular Values
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 Numerical linear a
From playlist MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Ana Balibanu: The partial compactification of the universal centralizer
Abstract: Let G be a semisimple algebraic group of adjoint type. The universal centralizer is the family of centralizers in G of regular elements in Lie(G), parametrized by their conjugacy classes. It has a natural symplectic structure, obtained by Hamiltonian reduction from the cotangent
From playlist Algebra
In this tutorial we take a look at elementary matrices. They start life off as identity matrices to which a single elementary row operation is performed. They form the building blocks of Gauss-Jordan elimination. In a future video we will use the to do LU decomposition of matrices.
From playlist Introducing linear algebra
Understanding Matrices and Matrix Notation
In order to do linear algebra, we will have to know how to use matrices. So what's a matrix? It's just an array of numbers listed in a grid of particular dimensions that can represent the coefficients and constants from a system of linear equations. They're fun, I promise! Let's just start
From playlist Mathematics (All Of It)
Microlocal sheaves on certain affine Springer fibers - Zhiwei Yun
Geometric and Modular Representation Theory Seminar Topic: Microlocal sheaves on certain affine Springer fibers Speaker: Zhiwei Yun Affiliation: Massachusetts Institute of Technology Date: April 14, 2021 For more video please visit http://video.ias.edu
From playlist Seminar on Geometric and Modular Representation Theory
Vector and matrix forms for systems of linear equations | Linear Algebra MATH1141 | N J Wildberger
A system of linear equations may also be viewed in vector form, as an attempt to write one vector as a linear combination of other vectors. Or it more alternatively be viewed in matrix form. We discuss the matrix of coefficients, the vector of variables and the vector of constants. Puttin
From playlist Higher Linear Algebra
We have already looked at the column view of a matrix. In this video lecture I want to expand on this topic to show you that each matrix has a column space. If a matrix is part of a linear system then a linear combination of the columns creates a column space. The vector created by the
From playlist Introducing linear algebra
What is a matrix? Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Matrices
matrix choose a matrix. Calculating the number of matrix combinations of a matrix, using techniques from linear algebra like diagonalization, eigenvalues, eigenvectors. Special appearance by simultaneous diagonalizability and commuting matrices. In the end, I mention the general case using
From playlist Eigenvalues
The matrix approach to systems of linear equations | Linear Algebra MATH1141 | N J Wildberger
We summarize the matrix approach to solving systems of linear equations involving augmented matrices and row reduction. We also study the consequences of linearity of themultiplication of a matrix and vector. ************************ Screenshot PDFs for my videos are available at the webs
From playlist Higher Linear Algebra
Lecture 01-03 Linear Algebra review
Machine Learning by Andrew Ng [Coursera] 0113 Matrices and vectors 0114 Addition and scalar multiplication 0115 Matrix-vector multiplication 0116 Matrix-matrix multiplication 0117 Matrix multiplication properties 0118 Inverse and transpose
From playlist Machine Learning by Professor Andrew Ng
The Diagonalization of Matrices
This video explains the process of diagonalization of a matrix.
From playlist The Diagonalization of Matrices
After our introduction to matrices and vectors and our first deeper dive into matrices, it is time for us to start the deeper dive into vectors. Vector spaces can be vectors, matrices, and even function. In this video I talk about vector spaces, subspaces, and the porperties of vector sp
From playlist Introducing linear algebra
Part IV: Matrix Algebra, Lec 2 | MIT Calculus Revisited: Multivariable Calculus
Part IV: Matrix Algebra, Lecture 2: The "Game" of Matrices Instructor: Herbert Gross View the complete course: http://ocw.mit.edu/RES18-007F11 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Calculus Revisited: Multivariable Calculus