Advanced Linear Algebra - Advanced Linear Algebra, Lecture 7.1: Definiteness and indefiniteness
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.
Advanced Linear Algebra, Lecture 7.1: Definiteness and indefiniteness
A matrix M is positive-definite, or just positive, if (x,Mx) is positive for all nonzero x. We can similarly define what it means to be nonnegative, negative, and nonpositive. These are equivalent to the eigenvalues of M being positive, nonnegative, negative, and nonpositive, respectively. A matrix is said to be indefinite if it is none of these, i.e., if it has both positive and negative eigenvalues. We prove some basic properties about positive maps, such as that they always have a unique square root. Finally, we show that in the space of self-adjoint maps, the set of positive maps is open, and its closure are the nonnegative maps.
Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html
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