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Advanced Linear Algebra, Lecture 3.6: Minors and cofactors
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 3.6: Minors and cofactors

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 3.6: Minors and cofactors The most common algorithm for computing determinants involves crossing out the i'th row and j'th column to obtain an (n-1)x(n-1) submatrix A_{ij}. The (i,j) minor is the determinant of this matrix and the (i,j) cofactor is this times (-1)^{i+j}. In this lecture, we derive the popular Laplace expansion, which says that det(A) is the linear combination of cofactors by the coefficients, down any column or across any row. We also see how Cramer's rule gives a formula for the solution of a system Ax=b in terms of cofactors and det(A). This also gives us a simple formula for the inverse of a matrix. Unfortunately, these formulas are not practical because computing determinants is a computationally expensive task. However, we will need these formulas for proofs later on. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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