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Random Matrix Theory and its Applications - Lecture 2

Offered By: International Centre for Theoretical Sciences via YouTube

Tags

Random Matrix Theory Courses Quantum Mechanics Courses Linear Algebra Courses Eigenvalues Courses Eigenvectors Courses

Course Description

Overview

Delve into the second lecture of Random Matrix Theory and its Applications, presented by Satya Majumdar at the Bangalore School on Statistical Physics - X. Explore key concepts in linear algebra and quantum mechanics, including operators, orthogonal transformations, and eigenvectors. Learn about random matrices, their ensembles, and the Gaussian Ensemble. Examine rotation-invariant ensembles and their properties. Engage with examples, exercises, and a Q&A session to deepen your understanding of this advanced topic in statistical physics.

Syllabus

Random Matrix Theory and its Applications: Recap
1. Basics of linear algebra/quantum mechanics
2. Operator
3. Orthogonal transformation
Under this orthonormal transformation
Similarity transformation
4. Eigenvectors & eigenvalues of H Hat
How does one find eigenvalues & eigenvectors?
H Hat =[Hij] -NxN matrix: Matrices with real eigenvectors
Random matrix
Define Random Matrix
Ensembles of random matrices
Wigner matrices
Example
Q: What is the joint distribution of eigenvalues?
Example: Gaussian Ensemble
Rotation invariant ensembles
Under any orthonormal transformation
Example
Exercise: Prove it for NxN case
Q&A


Taught by

International Centre for Theoretical Sciences

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