Numerical Optimization, Jan-May 2022

Credits 9 credits

Instructor

Mailing list

Exams and Grading

Online Lectures

Click here for accessing the course materials

Assignments

• All assignments need to be submitted before Sunday midnight.
• There will be both written and computational part in the assignments.
• We will be relying on MATLAB/Octave for the computing part of the assignment.
• Students are strongly encouraged to typeset their solution via LaTeX/TeX. Typesetting using LaTeX/TeX will obtain 25% bonus on the score they get for the assignment.
• Students need to submit their assignments through the dropbox link provided.
• The name of the zipped file (this should contain the LaTeX/TeX source file, pdf, and MATLAB code) for submitting your assignment should be as follows: ma14m093_5.zip (or .rar or .tar), where ma14m093 is your roll number and 5 implies that you are submitting your fifth assignment.
• Any copying on assignments will result in a zero on the assignment.
• We will be using JPlag to detect similarities among multiple sets of source code files.
• The grader will expect you to express your ideas clearly, legibly, and completely, often requiring complete English sentences rather than merely just a long string of equations or unconnected mathematical expressions. This means you could lose points for poorly written proofs or answers. Clear exposition is a crucial ingredient of technical communication. Clarity of thought and presentation is more important in mathematics & sciences than any other field. The only way to master exposition is by repeated practicing.

Short Syllabus

Textbooks

• Numerical Optimization by Nocedal and Wright
• Algorithms for Optimization by Mykel J. Kochenderfer and Tim A. Wheeler
• Linear and Nonlinear Programming by David G. Luenberger and Yinyu Ye
• Practical Methods of Optimization by R. Fletcher
• Nonlinear Programming by Dimitri P. Bertsekas