EECS260 Optimization (Fall semester 2016)


Miguel Á. Carreira-Perpiñán
Electrical Engineering and Computer Science
School of Engineering
University of California, Merced
mcarreira-perpinan-[at]; 209-2284545
Office: 217, Science & Engineering Building 2

Office hours: Tuesdays 2:45-3:45pm (SE2-217).

Lectures: Mondays/Wednesdays 4:30-5:45pm (COB2-264).

Lab class: Thursdays 7:30-10:20pm (Linux Lab, SE1-138).

Course web page:

Course description

Optimization problems arise in multiple areas of science, engineering and business. This course introduces theory and numerical methods for continuous multivariate optimization (constrained and unconstrained), including: line-search and trust-region strategies; conjugate-gradient, Newton, quasi-Newton and large-scale methods; linear programming; quadratic programming; penalty and augmented Lagrangian methods; sequential quadratic programming; and interior-point methods. The primary programming tool for this course is Matlab.

Prerequisites: MATH 23, MATH 24, MATH 141 or equivalent (undergraduate courses in linear algebra and multivariate calculus), MATH 131 (numerical analysis I). Basic concepts will be briefly reviewed during the course.


Required textbook (get the errata and additional errata):

Other recommended books:

If you want to refresh your knowledge of linear algebra and multivariate calculus, the following are helpful (any edition or similar book is fine):

Syllabus and required textbook reading


Before each class, you should have read the corresponding part of the textbook and the notes. I will teach the material in the order below (which is more or less the order in the book).

Handouts and assignments

Course grading

The course grading will be based on three projects and a final exam, as follows (but note that too low a grade in the exams cannot be compensated by a high grade in the projects or vice versa):

While I encourage you to discuss your work with other students, the projects and the exam must be the result of your own work without collaboration.

I will also give homework exercises (mainly from the textbook) of two types, pencil-and-paper and Matlab programming. I will not ask you to solve them, i.e., they will not count towards your grade. I will give the solutions and solve some of the exercises in class. However, I strongly recommend that you try to solve all the exercises on your own.

Grade curves: midterm, final.

Optimization links

Matlab tutorials

If you have never used Matlab, there are many online tutorials, for example:

Other links

Miguel A. Carreira-Perpinan
Last modified: Fri Mar 17 15:56:50 PDT 2017

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