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

Random Matrix Theory and its Applications - Lecture 2

International Centre for Theoretical Sciences
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