Moduli theory | Complex analysis | Operator theory
In complex analysis and geometric function theory, the Grunsky matrices, or Grunsky operators, are infinite matrices introduced in 1939 by Helmut Grunsky. The matrices correspond to either a single holomorphic function on the unit disk or a pair of holomorphic functions on the unit disk and its complement. The Grunsky inequalities express boundedness properties of these matrices, which in general are contraction operators or in important special cases unitary operators. As Grunsky showed, these inequalities hold if and only if the holomorphic function is univalent. The inequalities are equivalent to the inequalities of Goluzin, discovered in 1947. Roughly speaking, the Grunsky inequalities give information on the coefficients of the logarithm of a univalent function; later generalizations by Milin, starting from the Lebedev–Milin inequality, succeeded in exponentiating the inequalities to obtain inequalities for the coefficients of the univalent function itself. The Grunsky matrix and its associated inequalities were originally formulated in a more general setting of univalent functions between a region bounded by finitely many sufficiently smooth Jordan curves and its complement: the results of Grunsky, Goluzin and Milin generalize to that case. Historically the inequalities for the disk were used in proving special cases of the Bieberbach conjecture up to the sixth coefficient; the exponentiated inequalities of Milin were used by de Branges in the final solution.A detailed exposition using these methods can be found in . The Grunsky operators and their Fredholm determinants are also related to spectral properties of bounded domains in the complex plane. The operators have further applications in conformal mapping, Teichmüller theory and conformal field theory. (Wikipedia).
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
This is a video I have been wanting to make for some time, in which I discuss what the quaternions are, as mathematical objects, and how we do calculations with them. In particular, we will see how the fundamental equation of the quaternions i^2=j^2=k^2=ijk=-1 easily generates the rule for
From playlist Quaternions
Complex Analysis (Advanced) -- The Schwarz Lemma
A talk I gave concerning my recent results on the Schwarz Lemma in Kähler and non-Kähler geometry. The talk details the classical Schwarz Lemma and discusses André Bloch. This is part 1 of a multi-part series. Part 1 -- https://youtu.be/AWqeIPMNhoA Part 2 -- https://youtu.be/hd7-iio77kc P
From playlist Complex Analysis
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
QR decomposition (for square matrices)
Support the channel on Steady: https://steadyhq.com/en/brightsideofmaths Official supporters in this month: - William Ripley - Petar Djurkovic - Mayra Sharif - Dov Bulka - Lukas Mührke - Khan El - Marco Molinari - Andrey Kamchatnikov - Benjamin Bellick - Sarah Kim This video is abou
From playlist Linear algebra (English)
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
What is a matrix? Free ebook http://tinyurl.com/EngMathYT
From playlist Intro to Matrices
Linear Algebra for Computer Scientists. 12. Introducing the Matrix
This computer science video is one of a series of lessons about linear algebra for computer scientists. This video introduces the concept of a matrix. A matrix is a rectangular or square, two dimensional array of numbers, symbols, or expressions. A matrix is also classed a second order
From playlist Linear Algebra for Computer Scientists
How do we add matrices. A matrix is an abstract object that exists in its own right, and in this sense, it is similar to a natural number, or a complex number, or even a polynomial. Each element in a matrix has an address by way of the row in which it is and the column in which it is. Y
From playlist Introducing linear algebra
How to Quickly Create a Matrix in GeoGebra; How to Multiply 2 Matrices
Creating a matrix in GeoGebra is EASY. You need to use the LIST icons { }. In GeoGebra, a matrix is actually a sequence of lists within a single list. This video shows how.
From playlist Algebra 1: Dynamic Interactives!
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
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
Desmos Matrix Calc: Matrix Multiplication
This video explains how to us the Desmos Matrix Calculator to perform matrix multiplication. Not solved by hand
From playlist Introduction to Matrices and Matrix Operations
Eigenvectors and Eigenvalues with Jon Krohn
Data scientist Jon Krohn introduces the linear algebra concepts of Eigenvectors and Eigenvalues with a focus on Machine Learning and Python programming. This lesson is an excerpt from “Linear Algebra for Machine Learning LiveLessons” Purchase the entire video course at informit.com/youtub
From playlist Talks and Tutorials
We start discussing how to label matrices and their elements. We then define Order of Matrices and Equal Matrices working an example at 5:49. I then discuss Adding Matrices at 9:40 and work through three examples. Properties of Adding Matrices are explained at 16:00 Scalar Multiplication
From playlist Linear Algebra
Using a Matrix Equation to Solve a System of Equations
This video shows how to solve a system of equations by using a matrix equation. The graphing calculator is integrated into the lesson. http://mathispower4u.yolasite.com/ http://mathispower4u.wordpress.com/
From playlist Matrix Equations
This video defines the transpose of a matrix and explains how to transpose a matrix. The properties of transposed matrices are also discussed. Site: mathispower4u.com Blog: mathispower4u.wordpress.com
From playlist Introduction to Matrices and Matrix Operations
WildLinAlg12: Generalized dilations and eigenvectors
This video introduces the important idea of changing coordinates in Linear Algebra. A linear transformation can be described using many different matrices, depending on the underlying coordinate system, or ordered basis, which is used to describe the space. The simplest case is when the
From playlist A first course in Linear Algebra - N J Wildberger
Pauli matrices vs. su(2) basis vs. quaternions
In this video we discuss Pauli matrices as base for hermitean 2x2 complex matrices, as relevant for modeling observables in quantum theory - but also for quantum mechanics, as demonstrated. You can find the text used in this video here: https://gist.github.com/Nikolaj-K/103f07367c116b64b56
From playlist Physics