Syllabus for

We cover the principles of nonlinear continuous optimization, that is, minimizing an objective function that depends (non)linearly and continuously on unknown variables that satisfy constraints. Convex optimization will be introduced. Applications to data-mining and machine learning ("data science") will also be introduced.

Lectures start: Thursday Sept. 7 and end Tuesday Dec. 5, 2023.

Instructor: Henry Wolkowicz;     Time: Tues./Thurs. 4-5:20PM;     Room: MC4060
Email: Instructor: hwolkowicz@uwaterloo.ca;     Office Hours: Fri. 2-3PM, MC6312; Wed. 1-2PM on ZOOM, Meeting ID: to be annunced

TAs:

• Kartik Singh; k266sing@uwaterloo.ca; office hour Wed. 3PM in MC6311

Late or missed assignments are given a mark of zero.
A missed midterm will be given a mark of zero unless the cause is illness (a medical note is necessary), or some other serious reason given promptly in writing. In the latter case the corresponding weight will normally be transferred to the final exam.

• Requirements: Prerequisites: (One of CO 250/350, 352, 255/355) and MATH 128 with a grade of at least 70% or MATH 138 or 148.
• Textbooks: There is no required textbook for this course. Suggested readings and class lecture notes cover the material for the course. The following textbooks are suggest for supplemental readings:
  • Nocedal and S. J. Wright, Numerical Optimization, Springer-Verlag, New York, 2006.(Available online at the library.)
  • S. Boyd, L. Vandenberghe, Convex optimization, 2009. (Available online at the library as well as at URL: stanford.edu/class/ee364a/, Introduction to Convex Optimization)

PIAZZA

• Academic integrity: In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. [Check the Office of Academic Integrity for more information.]
• Grievance: A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70, Student Petitions and Grievances, Section 4. When in doubt, please be certain to contact the departments administrative assistant who will provide further assistance.
• Discipline: A student is expected to know what constitutes academic integrity to avoid com- mitting an academic offence, and to take responsibility for his/her actions. [Check the Office of Academic Integrity for more information.] A student who is unsure whether an action consti- tutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about rules for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate associate dean. For information on categories of of- fences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties, check Guidelines for the Assessment of Penalties.
• Appeals: A decision made or penalty imposed under Policy 70, Student Petitions and Grievances (other than a petition) or Policy 71, Student Discipline may be appealed if there is a ground. A student who believes he/she has a ground for an appeal should refer to Policy 72, Student Appeals.
• Note for students with disabilities: Access Ability Services, located in Needles Hall, Room 1401, collaborates with all academic departments to arrange appropriate accommodations for students with disabilities without compromising the academic integrity of the curriculum. If you require academic accommodations to lessen the impact of your disability, please register with AccessAbility Services at the beginning of each academic term.

Links

• Introduction to Opt. on Youtube
• Convex Optimization lectures and videos