Symmetric Matrices, Quadratic Forms and Matrix Norm 
Symmetric Matrices, Quadratic Forms and Matrix Norm
by Stanford / Stephen P. Boyd
Video Lecture 16 of 20
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Views: 4,082
Date Added: March 29, 2009

Lecture Description

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).

Course Index

Course Description

In this course, Professor Stephen P. Boyd gives 20 video lectures on the concepts of Linear Dynamical Systems. He gives an introduction to applied linear algebra and linear dynamical systems, with applications to circuits, signal processing, communications, and control systems. Topics include: Least-squares approximations 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. Control, reachability, state transfer, and least-norm inputs. Observability and least-squares state estimation. EE263 covers some of the same topics, but is complementary to, CME200.

Tags: Math, Math L.D.S.


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