MATH285 Optimization

Credits (ECTS):10

Course responsible:Ole Løseth Elvetun

Campus / Online:Taught campus Ås

Teaching language:Engelsk

Course frequency:Annually

Nominal workload:125 hours of theory (Lectures and self-study). 125 hours for discussion, exercies and exam preparation

Teaching and exam period:This course starts in the spring parallel. This course has teaching/evaluation in spring parallel.

About this course

The course gives an introduction to the field of optimization, where we will cover four main topics:

  • Basic concepts
    • Convexity
    • Lines and hyperplanes
    • Taylor’s theorem
  • Unconstrained optimization
    • Optimality conditions
    • Search methods (Gradient methods and Newton’s method)
  • Linear programming
    • Standard form
    • Inequalities and slack variables
    • Simplex method
    • Duality
  • Non-linear constrained optimization
    • Optimality conditions
    • Convex optimization
    • Solution algorithms

Learning outcome

The students are to learn the basic theory of optimization. More specifically, they are expected to:

  • Explain basic concepts and results from the theory
  • Solve simple problems analytically
  • Recognize different types of optimization problems
  • Be able to implement a set of known algorithms in order to solve optimization problems numerically
  • The teaching will be given as lectures and exercises with an assistant teacher present.
  • The students can either contact the teacher in his/her office, by telephone or by e-mail
  • MATH111, MATH112, MATH113, MATH280 and INF100/INF120.
  • Final written examination, 3.5 hours. A -E / failed

  • The external and internal examiner jointly prepare the exam questions and the correction manual. The external examiner reviews the internal examiner's examination results by correcting a random sample of candidate's exams as a calibration according to the Department's guidelines for examination markings.
  • Lectures: 4 hours per week. Exercises: 2 hours per week.
  • Special requirements in Science