Course Hive
Search

Welcome

Sign in or create your account

Continue with Google
or
Advanced Linear Algebra, Lecture 7.1: Definiteness and indefiniteness
Play lesson

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.

5.0 (4)
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

Summary

Keywords

Full Transcript

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

Course Hive

Continue this lesson in the app

Install CourseHive on Android or iOS to keep learning while you move.

Related Courses

FAQs

Course Hive
Download CourseHive
Keep learning anywhere