MIT / Science / Mathematics
Lecture : Four Special Matrices
By Gilbert Strang | Computational Science and Engineering I
Lecture 1 of 49
Rate this lecture -      More Lectures - Select Lecture...1 : Four Special Matrices2 : Key Ideas of Linear Algebra3 : Differential Eqns and Difference Eqns4 : Solving a Linear System5 : Delta Function Day6 : Recitation 27 : Eigenvalues Part 18 : Eigen Values part 2 and Positive Definite part 19 : Positive Definite Day10 : Recitation 311 : Springs and Masses12 : Oscillation13 : Recitation 414 : Finite Differences in Time15 : Least Squares part 216 : Graphs and Networks17 : Recitation 518 : Kirchhoffs Current Law19 : Exam Review20 : Recitation 621 : Trusses and A (T)CA22 : Trusses part 223 : Finite Elements in 1D part 124 : Recitation 725 : Finite Elements in 1D part 226 : Quadratic Cubic Elements27 : Element Matrices 4th Order Bending Equations28 : Recitation 829 : Boundary Conditions Splines Gradient Divergence30 : Gradient and Divergence31 : Laplaces Equation32 : Recitation 933 : Laplaces Equation part 234 : Fast Poisson Solver part 135 : Fast Poisson Solver part 2 Finite Elements in 2D36 : Recitation 1037 : Finite Elements in 2D part 238 : Fourier Series part 139 : Recitation 1140 : Fourier Series part 241 : Discrete Fourier Series42 : Fast Fourier Transform Convolution43 : Recitation 1244 : Convolution part 2 Filtering45 : Filters Fourier Integral Transform46 : Fourier Integral Transform part 247 : Recitation 1348 : Convolution Equations Deconvolution49 : Sampling Theorem

Course Description
This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications.
Courses Index
1 : Introductory Probability and Statistics for Business   (Fletcher Ibser / Berkeley)
2 : Analytic Geometry and Calculus   (Thomas Scanlon / Berkeley)
3 : Introduction to Probability and Statistics   (Deborah NOLAN / Berkeley)
4 : Single Variable Calculus   (David Jerison / MIT)
5 : Multivariable Calculus   (Denis Auroux / MIT)
6 : Differential Equations   (Haynes Miller / MIT)
7 : Linear Algebra   (Gilbert Strang / MIT)
8 : Mathematical Methods for Engineers II   (Gilbert Strang / MIT)
9 : Multivariable Calculus   (Michael HUTCHINGS / Berkeley)
10 : Calculus I   (Richard Delaware / University of Missouri)
11 : College Algebra   (Richard Delaware / University of Missouri)
12 : Homework Help for Multivariable Calculus   (Multiple Instructors / MIT)
13 : Homework Help for Single Variable Calculus   (Multiple Instructors / MIT)
14 : Probability for Math Science   (Herbert Enderton / UCLA)
15 : Differential and Integral Calculus   (Steve Butler / UCLA)