Module 24-M-Opt Optimization for Quantitative Economics

Faculty

Person responsible for module

Regular cycle (beginning)

Every winter semester

Credit points and duration

7 Credit points

For information on the duration of the modul, refer to the courses of study in which the module is used.

Competencies

Non-official translation of the module descriptions. Only the German version is legally binding.

This module introduces the student to the mathematical foundations of (i) convergence : continuity of functions and the underlying topological structure in metric spaces and (ii) convexity and optimization and their use in economic models. The students learn about the foundations of calculus in metric spaces and convex analysis. This should provide a deep understanding as well as techniques, which allow the students to deal with optimization problems with and without constraints.

Content of teaching

The module consists in one lecture with the following content:

A. Convergence in metric spaces

Metric spaces, distance, norm on a vector space, open and closed sets, sequences in a metric space, continuity, uniform continuity. Compact sets in a metric space. Complete spaces, Contractions. Finite dimensional vector space. Complement to calculus : Frechet differentiability and the Implicit function theorem

B. Convexity and optimization

B.1. Convexity of sets and functions. Convex sets. Examples : budget sets, balls, production sets. Convex and concave functions, graph, epigraph and hypograph. Quasiconvex and quasiconcave functions. Strictly convex and quasi convex functions. Characterization of a convex funtion with its first order derivative. Characterization of a convex funtion with its second order derivative. Topological properties of convex sets. Projection on a closed convex set. Separation theorems. Orthogonality and polarity. The bipolar theorem. Farkas lemma.

B.2. Optimization under constraints

B.2.1. Unconstrained optimization. Global and local maximum (minimum). First order necessary conditions. Second order necessary condition and second order sufficient condition. Global maxima for concave (convex) functions. Examples.

B.2.2. Constrained optimization. Convexity conditions and Slater condition. The Kuhn-Tucker problem in convex programming (statement without proof). Applications of Kuhn-Tucker Theorem in consumer theory and producer theory. More examples of Applications of Kuhn-Tucker Theorem. Linear programming. Quadratic programming

Books:
Simon, C., Blume, L., Mathematics for Economists, (1994) Norton.De La Fuente, A., Mathematical Methods and Models for Economists, 2nd Ed. (2005) Cambridge University Press.

Recommended previous knowledge

Necessary requirements

Explanation regarding the elements of the module

Module structure: 1 SL, 1 bPr 1

Courses

Optimization
Type lecture
Regular cycle WiSe
Workload5 90 h (60 + 30)
LP 3 [Pr]
Excercise on Optimization
Type exercise
Regular cycle WiSe
Workload5 60 h (30 + 30)
LP 2 [SL]

Study requirements

Allocated examiner Workload LP2
Teaching staff of the course Excercise on Optimization (exercise)

Regular completion of exercises with a recognisable solution approach. Participation in exercise groups (presentation of calculation exercises twice when asked. The organiser may replace some of the exercises by exercises in attendance).

see above see above

Examinations

written examination o. oral examination
Allocated examiner Teaching staff of the course Optimization (lecture)
Weighting 1
Workload 60h
LP2 2

Written examination of usually 90 minutes or oral examination of usually 20-30 minutes.

The module is used in these degree programmes:

Degree programme Profile Recom­mended start 3 Duration Manda­tory option 4
Quantitative Economics / Master of Science [FsB vom 15.02.2013 mit Änderungen vom 01.07.2015 und 31.03.2023] 1. one semester Obli­gation
Quantitative Economics / Master of Science [FsB vom 15.02.2013 mit Änderungen vom 01.07.2015 und 31.03.2023] International Track 1. one semester Obli­gation

Automatic check for completeness

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Legend

1
The module structure displays the required number of study requirements and examinations.
2
LP is the short form for credit points.
3
The figures in this column are the specialist semesters in which it is recommended to start the module. Depending on the individual study schedule, entirely different courses of study are possible and advisable.
4
Explanations on mandatory option: "Obligation" means: This module is mandatory for the course of the studies; "Optional obligation" means: This module belongs to a number of modules available for selection under certain circumstances. This is more precisely regulated by the "Subject-related regulations" (see navigation).
5
Workload (contact time + self-study)
SoSe
Summer semester
WiSe
Winter semester
SL
Study requirement
Pr
Examination
bPr
Number of examinations with grades
uPr
Number of examinations without grades
This academic achievement can be reported and recognised.