Students should develop a working knowledge of convex optimization, acquiring a background and a skill set needed to recognize, formulate, analyze and solve convex optimization problems.
Introduction to optimization; convex sets and convex functions; formulations of convex optimization problems; duality theory and optimality conditions; 1st- and 2nd-order algorithms and applications
Matrix analysis, multi-variable calculus/analysis, Matlab programming.
Homework assignments will be posted online, roughly weekly, at the course website. Please check weekly. Late submissions are not accepted unless explicitly permitted by the instructor under special circumstances.
Assignments will consist of theoretical problems (requiring mathematical proofs) and computational problems (involving algorithm implementation and problem-solving on computer). Theoretical problems are generally ungraded with their solutions available in the Solution Manuals. Computer problems will be graded.
While students can discuss about the assigned problems with others, they must write out the solutions and programs individually by themselves (no sharing of identical solutions or copying codes from each others).
Two exams are planned: a midterm (in-class) and a final (take-home). A part of the final may include problem-solving on computer.
Final grades will be based on the weighted average of the homework assignments (30%), and mid-term exams (30%) and the final (40%). Class attendance/participation will be a factor in determining borderline cases, and excessive absence will result in a penalty.
The above information may be subject to change
with reasonable advance notice, as deemed appropriate by the instructor.
Any student with a disability requiring accommodation in this course is encouraged to contact the instructor during the first week of class, and also to contact Disability Support Services in the Ley Student Center.