Topics in Discrete Numerical Methods

BEA516 Topics in Discrete Numerical Methods

  • Topics

    Topics

    Topics will be lectured in the following sequence

    1. Algorithms for convex models, linear and nonlinear
    2. Algorithms for non-convex and combinatorial models
    3. Relaxation and partitioning methods
    4. Algorithms for stochastic models

  • Learning outcome

    Learning outcome

    The objective of the course is to provide students with the capability to formulate and solve problems in economics and management science using tools of discrete optimization. Deterministic as well as stochastic problems are emphasized and particular models involving non-convexities are considered. Relaxation and partitioning methods are considered and the use of duality theory in the construction of efficient numerical methods are described.

     

    Skills

    The candidates should be able to

    • Read relevant scientific papers addressing discrete optimization problems
    • Formulate and model operational management tasks and assign feasible numerical solution schemes
    • Communicate and bridge the gap between theoretical economic modeling and feasible real worlds approaches
    • Understand how non-convexities can be modeled and how they affect the efficiency of various numerical solution approaches

     

    Competence

    The candidates can

    • Manage complex interdisciplinary research projects involving discrete optimization models
    • Recognize the potential as well as the limitations of modern numerical solution approaches in the field
    • Select appropriate modeling techniques and solution approaches to discrete optimization models of real life economic and management problems

  • Required prerequisites

    Required prerequisites

     Knowledge of medium advanced calculus

  • Requirements for course approval

    Requirements for course approval

     

  • Assessment

    Assessment

    Based on

    • Activity in class
      • Three assignments
      • Short oral examination at the end of the course

  • Grading Scale

    Grading Scale

     Pass/Fail

  • Computer tools

    Computer tools

    High level modeling languages as GAMS or AMPL will be used

  • Semester

    Semester

    Spring

     

     

     

  • Literature

    Literature

    All topics of the course are covered by scientific papers and selected parts of advanced textbooks such as

    • Bertsekas D, Convex Analysis and Optimization Athena Press 2003
    • Boyd S., Vandenberghe L. Convex Optimization Stanford University Press

Overview

ECTS Credits
5
Teaching language
English
Semester
Spring, Autumn

Course responsible

Mikael Rönnqvist, Department of Business and Management Science