16
Diagonalizability
Lecture no. 16 from the course: Mastering Linear Algebra: An Introduction with Applications
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Taught by Professor Francis Su | 32 min | Categories: Default Category
In this third lecture on eigenvectors, examine conditions under which a change in basis results in a basis of eigenvectors, which makes computation with matrices very easy. Discover the property called diagonalizability, and prove that being diagonalizable is the equivalent to having a basis of eigenvectors. Also explore the connection between the eigenvalues of a matrix and its determinant.
24 Lectures
1
Linear Algebra: Powerful Transformations
0
of 28 min
2
Vectors: Describing Space and Motion
0
of 27 min
3
Linear Geometry: Dots and Crosses
0
of 28 min
4
Matrix Operations
0
of 31 min
5
Linear Transformations
0
of 28 min
6
Systems of Linear Equations
0
of 28 min
7
Reduced Row Echelon Form
0
of 28 min
8
Span and Linear Dependence
0
of 31 min
9
Subspaces: Special Subsets to Look For
0
of 29 min
10
Bases: Basic Building Blocks
0
of 29 min
11
Invertible Matrices: Undoing What You Did
0
of 30 min
12
The Invertible Matrix Theorem
0
of 34 min
13
Determinants: Numbers That Say a Lot
0
of 30 min
14
Eigenstuff: Revealing Hidden Structure
0
of 27 min
15
Eigenvectors and Eigenvalues: Geometry
0
of 29 min
16
Diagonalizability
0
of 32 min
17
Population Dynamics: Foxes and Rabbits
0
of 30 min
18
Differential Equations: New Applications
0
of 33 min
19
Orthogonality: Squaring Things Up
0
of 32 min
20
Markov Chains: Hopping Around
0
of 33 min
21
Multivariable Calculus: Derivative Matrix
0
of 31 min
22
Multilinear Regression: Least Squares
0
of 28 min
23
Singular Value Decomposition: So Cool
0
of 32 min
24
General Vector Spaces: More to Explore
0
of 34 min
