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
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 () and project (). 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. Hardcopy of each set of notes will be handed out in class as we progress.
Math Corner 380-380W
First class: Mon March 28, 2016
Auditors are welcome
Instructor: Prof Saunders, Huang M03