My notes for when I took this course in Fall 2025, taught by Somayeh Sojoudi.
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💡
Please submit any errors you might find in the errata, thank you!
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Phoenix Wilson
Shout out to George Ma for some of the illustrations used here!
Course by Week
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The notes on the linear algebra module are not comprehensive.
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| Week |
Topics |
| 1 |
Standard Form, Minima, Feasibility |
| 2 |
Affine Sets, Projections |
| 3 |
Hyperplanes |
| 4 |
Spectral Theorem, Sign Definite Matrices, Multivariate Taylor Expansions, Hessians |
| 5 |
Singular Value Decomposition, Moore-Penrose Pseudoinverse, Matrix Norms, Low Rank Factorization |
| 6 |
Low-Rank Approximation, PCA, Sensitivity Analysis |
| 7 |
Ellipsoids, Least Squares Perturbation, Elementary Topology |
| 8 |
Convex Sets, Convex Functions |
| 9 |
Convex Optimization, Coercive Functions |
| 10 |
Linear Programs, Superlinear Programs, Convex Relaxation |
| 11 |
Integer Programs, Compressed Sensing, Lasso |
| 12 |
Slater’s Condition, Karush-Kuhn-Tucker, Lagrangian |
| 13 |
Duality |
| 14 |
Infeasibility Certificates, Constraint Elimination, Sensitivity Parameters |
| 15 |
Matrix Optimization, Iterative Methods |
Standard Form, Minima, Feasibility
Affine Sets, Projections
Hyperplanes
Spectral Theorem, Sign Definite Matrices, Multivariate Taylor Expansions, Hessians
Singular Value Decomposition, Moore-Penrose Pseudoinverse, Matrix Norms, Low Rank Factorization
Low-Rank Approximation, PCA, Sensitivity Analysis
Ellipsoids, Least Squares Perturbation, Elementary Topology
Convex Sets, Convex Functions
Convex Optimization, Coercive Functions
Linear Programs, Superlinear Programs, Convex Relaxation
Integer Programs, Compressed Sensing, Lasso