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Advanced Linear Algebra, Lecture 6.2: Spectral resolutions
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 6.2: Spectral resolutions

Unlock the Power of Vector Spaces: Master Advanced Linear Algebra with Professor Macauley. Dive deep into theory and applications, from eigenvectors to spectral theorems. Enhance your mathematical prowess and transform complex problems into elegant solutions.

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What you'll learn

Understand key concepts of vector spaces, including spanning, independence, and bases.
Analyze the role of eigenvalues, eigenvectors, and the spectral theorem in linear mappings.
Apply the Gram-Schmidt process and orthogonal projection in various contexts.
Evaluate the properties and applications of quadratic forms and spectral resolutions.

This course includes

  • 25.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

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Advanced Linear Algebra, Lecture 6.2: Spectral resolutions We begin this lecture by showing that every self-adjoint map H:X→X has only real eigenvalues, and an orthonormal basis of eigenvectors. As a result, it is diagonalizable by a unitary map U (i.e., a distance-preserving map, equivalently characterized by U*U=I). By the spectral theorem, X is a direct sum of H-eigenspaces, and the identity map can be written as a sum of the projection onto each eigenspace. This is called a resolution of the identity. Similarly, H can be written as a linear combination of these projection maps, called its spectral resolution. This allows us to easily define non-polynomial functions on H, as long as they are defined on the eigenvectors. This avoids the issue of convergence, which would arise if we tried to define something like the exponential function of H by plugging it into its Taylor series. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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