filmov
tv
Advanced Linear Algebra, Lecture 2.7: Change of basis

Показать описание
Advanced Linear Algebra, Lecture 2.7: Change of basis
If T is a linear map from X to U, and X≅R^n and U≅R^m, then we can define bijections B from X to R^n, and C from R^m, sending our bases of X and U to the standard bases. This defines a linear map N=CTB^{-1} from R^n to R^m, and hence a basis. If X=U, then this means that C=B, and N and T are similar. Along these lines, similar matrices can represent the same linear map but with respect to a different choice in basis. If B=P^{-1}AP, then P is a "change of basis matrix", and we see an explicit example of how to construct this in the 2x2 case, and the generalization to larger matrices should be apparent. Thought this lecture, we rely on commutative diagrams to illustrate these similarity transforms.
If T is a linear map from X to U, and X≅R^n and U≅R^m, then we can define bijections B from X to R^n, and C from R^m, sending our bases of X and U to the standard bases. This defines a linear map N=CTB^{-1} from R^n to R^m, and hence a basis. If X=U, then this means that C=B, and N and T are similar. Along these lines, similar matrices can represent the same linear map but with respect to a different choice in basis. If B=P^{-1}AP, then P is a "change of basis matrix", and we see an explicit example of how to construct this in the 2x2 case, and the generalization to larger matrices should be apparent. Thought this lecture, we rely on commutative diagrams to illustrate these similarity transforms.
Advanced Linear Algebra, Lecture 2.7: Change of basis
Advanced Linear Algebra - Lecture 2: Subspaces
Advanced Linear Algebra 7: Properties of Linear Transformations
Advanced Linear Algebra - Lecture 7: The Dimension of a Vector Space
Advanced Linear Algebra 1: Vector Spaces & Subspaces
Advanced Linear Algebra, Lecture 7.1: Definiteness and indefiniteness
Advanced Linear Algebra, Lecture 1.2: Spanning, independence, and bases
Advanced Linear Algebra, Lecture 5.7: The norm of a linear map
Advanced Linear Algebra 4: Dimension of a Vector Space
Advanced Linear Algebra, Lecture 2.5: The transpose of a linear map
Advanced Linear Algebra 20: Positive Definite & Positive Semidefinite Matrices
Advanced Linear Algebra, Lecture 2.4: The four subspaces
Grant Sanderson (3Blue1Brown): Best Way to Learn Math | AI Podcast Clips
Advanced Linear Algebra 8: The Half Derivative
How to eat Roti #SSB #SSB Preparation #Defence #Army #Best Defence Academy #OLQ
Memorization Trick for Graphing Functions Part 1 | Algebra Math Hack #shorts #math #school
Advanced Linear Algebra 3: Bases
Advanced Linear Algebra - Lecture 9: Linear Transformations
Linear transformations | Matrix transformations | Linear Algebra | Khan Academy
Advanced Linear Algebra, Lecture 2.1: Rank and nullity
Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra
Advanced Linear Algebra, Lecture 4.4: Invariant subspaces
How to self study pure math - a step-by-step guide
The Big Picture of Linear Algebra
Комментарии