Math 173

Schedule | Info

Week Topic Due
September 3
(No Class)
Review Intermediate Linear Algebra: finite dimensional vector spaces, inner product spaces, matrices and linear maps, Graham-Schmidt, Jordan Canonical Form, diagonalization and the Spectral Theorem, etc. Nothing.
September 10 Introduction. Fundamentals and Duality; Least Squares. Zorn's Lemma. Read: Ch. 1, 2
September 17 Linear Mappings. Spectral Theory. Singular Value Decomposition and PCA. Schur's Theorem. Read: Ch. 3, 6
Problem Set 1 (tex)
September 24 Euclidean Structure and Hilbert Space. Spectral Theory of Self-Adjoint Mappings and Definiteness. Covariance and Quadratic forms. Read: Ch. 7, 8
Problem Set 2 (tex)
October 1 Normed Linear Spaces. Metrics for Data. Banach spaces. Maps between normed spaces. Matrix calculus and applications. Read: Ch. 14, 15
Problem Set 3 (tex)
October 8 Complex Hahn-Banach Theorem. Perron-Frobenius Theorem. Google's PageRank algorithm. Read: Ch. 16
Problem Set 4 (tex)
October 15 Concentration. Johnson-Lindenstrauss Lemma and applications to big-data algorithm design. Randomized trace and Schatten-p norm estimators. (notes) Read: Woodruff Sec. 6.1
Watch: This Lecture
October 22 (no class) Enjoy break!
October 29 (no class) Work on midterm project.
November 5 Convexity. Hahn-Banach Separation Theorem. Caratheodory Theorem. Helly's Theorem. Birkhoff Theorem and applications to optimizing over permuation group. Read: Ch. 12
Small Problem Set 5 (tex)
Midterm Project (info)
November 12 Convex optimization. Analysis of iterative optimization algorithms via linear algebra techniques. Chebyshev iteration for solving large linear systems. Read: Ch. 17
Problem Set 6 (tex)
November 19 Matrix Sketching. Least Squares Problems and Johnson-Lindenstrauss Based Solutions. Read: Woodruff Intro
Problem Set 7 (tex)
Literature Review (info)
November 26 Farkas-Minkowski Theorem and Duality. Compressed Sensing by Linear Programming and a proof of correctness. Read: Ch. 13
Watch: This Lecture
December 3 Lagrange Duality and examples. Slater's Condition. NP-Hard Set Partitioning Problem, Semidefinite Program Lower Bound via the Dual, analytical bound on Duality Gap. (notes) Read: These Slides
Work on final project.
December 10 Come to class for consulting on final project by teaching staff! Work on final project.
December 17 Presentations. Final Report (in class)
Presentation (in class)