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Advanced Linear Algebra, Lecture 6.3: Normal linear maps
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 6.3: Normal linear maps

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 6.3: Normal linear maps Previously, we showed that commuting diagonalizable linear maps are simultaneously diagonalizable, which means that they share a common basis of eigenvectors. If these maps are self-adjoint, then they share a common orthonormal basis of eigenvectors, or in our language, a common spectral resolution. This is key to understanding precisely which linear maps have an orthonormal basis of eigenvectors, besides self-adjoint, anti-self-adjoint, orthogonal, and unitary maps. The answer are linear maps that are normal, which means they commute with their adjoints. This condition is key because if we decompose a linear map into its adjoint and self-adjoint part, as M = (M+M*)/2 + (M-M*)/2 = H + A, then M*M=MM* is precisely what is needed for H and iA to be commuting self-adjoint maps, and then we apply our previous result. We conclude this lecture by establishing several properties of normal and unitary linear maps. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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