MS&E 318/CME 338: Large-Scale Numerical Optimization


The main algorithms and software for constrained optimization, emphasizing the sparse-matrix methods needed for their implementation. Iterative methods for linear equations and least squares. Interior methods. The simplex method. Basis factorization and updates. The reduced-gradient method, augmented Lagrangian methods, and SQP methods.

3 units, Spring (Michael Saunders), Grading basis ABCD/NP

Prerequisites: Basic numerical linear algebra, including LU, QR, and SVD factorizations, and an interest in MATLAB, sparse-matrix methods, and gradient-based algorithms for constrained optimization

Homework, etc

There will be 4 or 5 homework assignments and one somewhat more challenging project. MATLAB is used for computational exercises.

Grades will be assessed from the homework (60%) and project (40%). There is no mid-term or final exam.

There is no text book for the class. See ‘‘references’’ for background reading and a reminder of some of the sources out there. See ‘‘notes’’ for the topics to be covered in turn.


Lane History Corner 200-013
Mon Wed Fri 2:15–3:30pm

First class: Mon March 30, 2015
Last class: Wed June 3, 2015

Auditors are welcome

Office hours

Instructor: Prof Saunders, Huang M03
Most Mondays, Tuesdays, Wednesdays after 6pm

Course assistants: Anil Damle, Huang 063; Yuekai Sun, Huang 053