CO367 Fall 2017
    Principles of nonlinear continuous optimization, that is, minimizing an objective function that depends nonlinear 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.

Nonlinear Optimization ( Course Information/Syllabus)
Instructor: Henry Wolkowicz (MC6312;;519-888-4567x35589)

  • Time:T-R 4:00--5:20PM     (Thurs. Sept. 7 to Thurs. Nov 28, 2017)
  • Location: MC4064
  • Office Hour: Henry Wolkowicz: T ??? R ??? MC6312.
  • TA: ?????
  • Midterm: date ?????? closed book; no calculators.
  • FINAL EXAM: date ?????? ; closed book; no calculators
  • Marking Scheme: HW (6 assigns) 30%; Midterm 30%; Final 40%
  • see Waterloo LEARN for Assignments/Solutions, Partial Lecture Notes, etc...


  • Text - Class notes and:
    Prerequisites:
      One of CO 250, 352, 255) and MATH 128 with a grade of at least 70% or MATH 138 or 148.
      (Not open to General Mathematics students)


  • Last Modified:  Sunday 9 July 2017