Last planned examination: Spring 2016
Decision to discontinue this course: No information inserted
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 four fundamental 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. Lagrangian relaxation.
The overall purpose of the course is that the student should get well acquainted with basic concepts, theory, models and solution methods for optimization. Further, the student should get basic skills in modelling and computer based solving of various applied optimization problems.
No information inserted
In general:
Completed upper secondary education including documented proficiency in English corresponding to English B. And 28 university credits (hp) in mathematics.
More precisely for KTH students:
Passed courses in calculus, linear algebra, differential equations, mathematical statistics, numerical analysis.
The student should have documented knowledge corresponding to university courses in mathematical calculus and analysis, linear algebra, numerical analysis, differential equations and transforms, and mathematical statistics.
No information inserted
Linear and Nonlinear Programming by Nash and Sofer, McGraw-Hill, and some lecture notes.
A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
A written examination (TEN1; 6 hp). Optional homeworks give credit points on the exam.
No information inserted
No information inserted
Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.
Course web SF1841Mathematics, Technology
First cycle
SF2812, SF2822.
SF1841 is today identical to SF1811, with common lectures and examination.