Numerical Optimization, Jan-May 2022
Credits 9 credits
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Exams and Grading
Click here for accessing the course materials
- 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.
Unconstrained optimization, Convex optimization, Lagrange multipliers, Linear Programming, Duality
- 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