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Win, Moe, and Alan Edelman, 18.996 Random Matrix Theory and Its Applications, Spring 2004. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed 07 Jul, 2010). License: Creative Commons BY-NC-SA

Random Matrix Theory and Its Applications

Spring 2004

Wigner's semi-circle law and the Gaussian Unitary Ensemble.
A plot of the distribution of the eigenvalues of the Gaussian Unitary Ensemble. (Image by Prof. Alan Edelman.)

Course Highlights

This course features lecture notes that summarize the topics discussed and analyzed in class and survey papers.

Course Description

This course is an introduction to the basics of random matrix theory, motivated by engineering and scientific applications.


Course Description

This course provides a rigorous introduction to fundamentals of random matrix theory motivated by engineering and scientific applications while emphasizing the informed use of modern numerical analysis software. Topics include Matrix Jacobians, Wishart Matrices, Wigner's Semi-Circular laws, Matrix beta ensembles, free probability and applications to engineering, science, and numerical computing. Lectures will be supplemented by reading materials and expert guest speakers, emphasizing the breadth of applications that rely on random matrix theory and the current state of the art.

Additional topics will be decided based on the interests of the students. No particular prerequisites are needed though a proficiency in linear algebra and basic probability will be assumed. A familiarity with MATLAB® will also be useful.

Course Objectives

Although applications and analysis using random matrix methods have emerged over the past decade or so, there is a gap between the mathematical theories and understanding of it by engineers. This is primarily because the theory on random matrices, developed almost concurrently by mathematicians, statisticians, and physicists has not yet been as widely used by engineers for there to be a body of literature employing consistent notation in explaining the use of such random matrix based techniques.

Our objective, in this course, is to present the random matrix theories that engineers have successfully used so far in a manner that highlights the historical and intellectual connections between the applications in mathematics, statistics, physics, and engineering. Along the way, we will

  • Derive the eigenvalue density for sample covariance matrices
  • Derive Wigner's semicircle law using combinatorial, free probability and resolvent based approaches
  • Use MATLAB® to develop tests that assess whether a pair of random matrices is asymptotically free
  • Use the Marcenko-Pastur theorem to determine the empirical distribution function for some classes of random sample covariance matrices.

Besides the measurable learning objectives described above, the students will also

  • Understand the state of the art in the mathematics of finite dimensional random matrices
  • Understand the fundamental mathematics and intuition for the mathematics of infinite dimensional random matrices including the tools of free probability
  • Recognize the manner in which these results have been applied so far and be aware of the limitations of these techniques
  • Use numerical tools such as MATLAB® to understand more difficult open questions in random matrix theory.


    1 Introduction
    2 Matrix Jacobians (the 2x2 case)
    3 Matrix Jacobians (the 2x2 case) (cont.)
    4 Matrix Jacobians (the 3x3 case)
    5 Matrix Jacobians and Spherical Coordinates
    6 Matrix Jacobians with Wedge Products
    7 Mechanics of Wedging
    8 Plucker Coordinates
    9 Jacobians of Matrix Factorizations
    10 Jacobians of Matrix Factorizations (cont.)
    11 Householder Transformations and the Stiefel Manifold
    12 The Cauchy-Binet Theorem
    13 Matrix Jacobians Interpreted Using the Cauchy-Binet Theorem
    14 Telatar's Paper and the Cauchy-Binet Theorem
    15 Level Densities and the Cauchy-Binet Theorem
    16 Orthogonal Polynomials
    17 Matrix Integrals and Orthogonal Polynomials
    18 Hypergeometric Functions of Matrix Arguments
    19 Hypergeometric Functions of Matrix Arguments (cont.)
    20 Computing Multivariate Orthogonal Polynomials Symbolically
    21 Random Matrix Theory and Wireless Communications - I
    22 Random Matrix Theory and Wireless Communications - II
    23 Random Matrix Theory and Wireless Communications - III
    24 Projects
    25 Projects (cont.)
    26 Projects (cont.)
    27 Projects (cont.)
    28 Advances and Open Research Areas in Random Matrix Theory   Tell A Friend