Welcome to the class website for Optimization Theory and Applications. This course discusses topics in optimization which is concerned with the minimization or maximization of objective functions along with constraints on permissible solutions. The course covers different types of problems that can be treated in this form along with development of theory and algorithms for their solution. The course will also discuss motivations and applications in the sciences and engineering, decision problems, and machine learning methods. Please be sure to read the prerequisites and grading policies for the class. Also see the syllabus for more details.
Prerequisites:
A working knowledge of calculus and linear algebra will be assumed.
Exams:
A written midterm exam will be given in class Thursday, May 8th.
- Midterm Outline of Topics [PDF].
Supplemental Materials:
- Python General Tutorial | Python tutorial at Codecademy | Python 3.11 documentation
- Anaconda Python Environment | Virtual Environment
- Numpy Python Package Tutorial | Jupyter Notebooks: Python Interface
- Python codes:
- Example code: Neville's method: [PDF] [python code] [jupyter notebook]
- LaTeX table writer: [PDF] [python code] [jupyter notebook]
- CSV table writer: [PDF] [python code] [jupyter notebook]
- generate markdown within jupyter notebooks: [PDF] [jupyter notebook]
- read/write .mat data files: [python code].
- Logistic regression loss (f_rlog): [python code].
- Book CLZ data and codes: [link].
- Book CLZ related codes translated to python:
- Notes:
- Introduction to Nonlinear Optimization [PDF]
- Linear Programming and Simplex Method [PDF]
- Simplex Method Implementation [PDF] [python code]
Homework Assignments:
Turn all homeworks in by Canvas by 5pm on the due date.
- Midterm Outline of Topics [PDF].
- Notes
- Introduction to Nonlinear Optimization [PDF]
- Linear Programming and Simplex Method [PDF]
- Simplex Method Implementation [PDF] [python code]
Grader this quarter will be Daniel Naylor (contact email on Canvas).
Numbered exercises are labelled as follows:
(CLZ): An Introduction to Optimization (fifth edition) by E. Chung, W. Lu, S. Zak, (data and codes [link]). From CLZ, unless otherwise noted.
HW1: (Due Fri, Apr 11) Ch 2: 1,3,11,16; Ch 5: 2,3,8; Ch 20: 1,2,3,4,8,12; Ch 21: 1,2,6,7.
HW2: (Due Wed, Apr 23) Ch 3: 1,4,14; Ch 20: 5,6,18,21; Ch 21: 9,11,17,23.
HW3: (Due Fri, May 2) Ch 4: 3,4,7; Ch 15: 1,3,5,10; Ch 16: 2,3,6.
HW4: (Due Fri, May 16) Ch 16: 5,8,10; Ch 17: 1,3,6,16,21,23,24.