SF1861 Optimization 6.0 credits


  • Education cycle

    First cycle
  • Main field of study

  • Grading scale

    A, B, C, D, E, FX, F

Course offerings

Spring 19 for programme students

Spring 20 CFATE m.fl. for programme students

Intended learning outcomes

The overall purpose of the course is that the student should be acquainted with basic concepts, theory, models and solution methods for optimization. Further, the student should get strengthen skills in linear algebra, and get basic skills in modelling and computer based solving of various applied optimization problems.

Course main content

Examples of applications and modelling training.

Basic concepts and theory for optimization, in particular theory for convex problems.

Some linear algebra in R^n, in particular bases for the fourfundamental subspaces corresponding to a given matrix, and LDLT-factorization of a symmetric definite matrix.

Linear optimization, including duality theory.

Optimization of flows in networks.

Quadratic optimization with linear constraints.

Linear least squares problems, in particular minimum norm solutions.

Unconstrained nonlinear optimization, in particular nonlinear least squares problems.

Optimality conditions for constrained nonlinear optimization, in particular for convex problems.


SF1618 + SF1619 Calculus and linear algebra.


Linear and Nonlinear Programming by Nash and Sofer, McGraw-Hill, and some lecture notes in Swedish.


  • HEM1 - Assignments, 1.5, grading scale: P, F
  • TEN1 - Examination, 4.5, grading scale: A, B, C, D, E, FX, F

Requirements for final grade

A written exam and home assignments.

Offered by



Per Enqvist <penqvist@kth.se>

Add-on studies

SF2812 Applied Linear Optimization, SF2822 Applied Nonlinear Optimization


Course syllabus valid from: Autumn 2008.
Examination information valid from: Autumn 2007.