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Advanced Linear Algebra, Lecture 3.5: The determinant and trace of a matrix
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 3.5: The determinant and trace of a matrix

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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43 learners

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 3.5: The determinant and trace of a matrix We begin this lecture by using n-linearity to give explicit formulas for the determinant of a 2x2 and 3x3 matrix, and it is easy to see how this generalizes to an nxn matrix. Specifically, the determinant is the sum of the determents of all n! permutation matrices, but with the original coefficients a_{ij} in the non-zero entries. This gives an easy way to see why a matrix and its transpose have the same determinant. We also see the definition of the trace of a matrix -- the sum of the diagonal entries. Though this can be defined for a general linear map, it involves eigenvalues, which we have not yet seen. We prove several basic properties of the trace: it is linear, it is commutative (i.e., tr(AB)=tr(BA)), and that similar matrices have the same trace and determinant. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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