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Stanford EE263: Introduction to Linear Dynamical Systems →0/20
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Introduction to Linear Dynamical Systems

1:16:46
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Introduction to Linear Dynamical Systems

1:05:52
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Introduction to Linear Dynamical Systems

1:19:10
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Introduction to Linear Dynamical Systems

1:14:08
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Introduction to Linear Dynamical Systems

1:15:14
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Introduction to Linear Dynamical Systems

1:16:19
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Introduction to Linear Dynamical Systems

1:15:46
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Least-Norm Solutions of Underdetermined Equations

1:15:58
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Autonomous Linear Dynamical Systems

1:09:02
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Autonomous Linear Dynamical Systems in Electrical Engineering

1:11:42
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Solutions via Laplace Transform and Matrix Exponentials

1:08:55
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Matrix Exponentials, Eigenvectors, and Diagonalization in LDS

1:13:37
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Generalized Eigenvectors, Diagonalization, and Jordan Canonical Form

1:13:02
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Applications of Jordan Canonical Form in LDS and Electrical Engineering

1:17:43
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Inputs, Outputs, and Symmetric Matrices in Linear Dynamical Systems

1:09:01
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Symmetric Matrices, Quadratic Forms, Matrix Norm, and SVDs in LDS

1:12:35
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Applications of Singular Value Decomposition in LDS and Electrical Engineering

1:16:52
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Applications of SVD, Controllability, and State Transfer in Electrical Engineering

1:15:14
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Controllability and State Transfer in Modern Electrical Engineering

1:10:30
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Observability and State Estimation

1:09:25

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

Stephen BoydDistinguishedStanford University

Professor

1:12:35·2008·graduate level

This lecture integrates symmetric matrices, quadratic forms, matrix norms, and Singular Value Decompositions (SVDs) to analyze linear dynamical systems. It highlights the power of these tools for system characterization.

MathematicsEngineeringLinear AlgebraSignal Processing & RoboticsControl & Dynamical SystemsSymmetric Matrices

Professor Stephen Boyd, of the Electrical Engineering department at Stanford University, lectures on the use of symmetric matrices, quadratic forms, matrix norm, and SVDs in LDS for the course Introduction to Linear Dynamical Systems (EE263).

Source: Stanford via YouTubeWatch on YouTube
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
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Inputs, Outputs, and Symmetric Matrices in Linear Dynamical Systems

Next · Lecture 17

Applications of Singular Value Decomposition in LDS and Electrical Engineering

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