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Advanced Linear Algebra, Lecture 5.2: Orthogonality
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 5.2: Orthogonality

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 5.2: Orthogonality Two vectors are orthogonal if their inner product is zero. This is the analogue of the concept of being perpendicular in Euclidean space to a general inner product space. Orthogonal bases are particular nice, because the coefficients of any linear combination of the basis vectors are simply a projection, and given by the formula a_i = (v, x_i)/(x_i,x_i). We derive this formula, do some example of what orthogonality means in a number of inner product vector spaces, including applications to Fourier series, and Sturm-Liouville theory, where the Legendre and Chebyshev polynomials arise. Our examples of these infinite dimensional functional spaces are meant to just be a tour, highlighting the diversity of applications of orthogonality in inner product spaces within math, science, and engineering. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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