Skip to content
lectern.tv
Stanford EE263: Introduction to Linear Dynamical Systems →0/20
1

Introduction to Linear Dynamical Systems

1:16:46
2

Introduction to Linear Dynamical Systems

1:05:52
3

Introduction to Linear Dynamical Systems

1:19:10
4

Introduction to Linear Dynamical Systems

1:14:08
5

Introduction to Linear Dynamical Systems

1:15:14
6

Introduction to Linear Dynamical Systems

1:16:19
7

Introduction to Linear Dynamical Systems

1:15:46
8

Least-Norm Solutions of Underdetermined Equations

1:15:58
9

Autonomous Linear Dynamical Systems

1:09:02
10

Autonomous Linear Dynamical Systems in Electrical Engineering

1:11:42
11

Solutions via Laplace Transform and Matrix Exponentials

1:08:55
12

Matrix Exponentials, Eigenvectors, and Diagonalization in LDS

1:13:37
13

Generalized Eigenvectors, Diagonalization, and Jordan Canonical Form

1:13:02
14

Applications of Jordan Canonical Form in LDS and Electrical Engineering

1:17:43
15

Inputs, Outputs, and Symmetric Matrices in Linear Dynamical Systems

1:09:01
16

Symmetric Matrices, Quadratic Forms, Matrix Norm, and SVDs in LDS

1:12:35
17

Applications of Singular Value Decomposition in LDS and Electrical Engineering

1:16:52
18

Applications of SVD, Controllability, and State Transfer in Electrical Engineering

1:15:14
19

Controllability and State Transfer in Modern Electrical Engineering

1:10:30
20

Observability and State Estimation

1:09:25

Least-Norm Solutions of Underdetermined Equations

Stephen BoydDistinguishedStanford University

Professor

1:15:58·2008·graduate level

This lecture delves into the methodology for finding least-norm solutions to underdetermined equations, a crucial concept in applied linear algebra. It specifically examines scenarios where the number of variables exceeds the number of independent equations.

MathematicsEngineeringLinear AlgebraSignal Processing & RoboticsControl & Dynamical SystemsLeast-Norm Solutions

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the least norm solutions of undetermined equations for the course, Introduction to Linear Dynamical Systems (EE263).

Introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares aproximations of over-determined equations and least-norm solutions of underdetermined equations. Symmetric matrices, matrix norm and singular value decomposition. Eigenvalues, left and right eigenvectors, and dynamical interpretation. Matrix exponential, stability, and asymptotic behavior. Multi-input multi-output systems, impulse and step matrices; convolution and transfer matrix descriptions.

LecturerAll lecturers →
Stephen BoydDistinguished

Samsung Professor of Engineering, Stanford & Co-Author of Convex Optimization

Samsung Professor of Engineering at Stanford whose textbook and EE364a lectures established convex optimization as a core language across control, signal processing, and ML.

2 courses · 43 lecturesCanon
Previous · Lecture 7

Introduction to Linear Dynamical Systems

Next · Lecture 9

Autonomous Linear Dynamical Systems

Comments

Add a comment...Sign in