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.
History Corner (Building 200) Room 203]
First class: Mon March 31
Auditors are welcome
Instructor: Prof Saunders, Huang M03