Advanced Numerical Computation

Lecturer (Coordinator):
Vicente Martín
Esther Dopazo
Juan Pedro Brito
José Luis Rosales


Second semester




The studied numerical techniques focus on optimization methods and are addressed from an essentially practical viewpoint. They include classical methods for problems with and without constraints, stochastic methods like simulated annealing and techniques based on biological systems like evolutionary computing or artificial immune systems, and foraging and swarm strategies. Finally, students will look at their application to production line problems in industry.

Learning Goals

Familiarity with applied advanced numerical calculus techniques and their implementation in high-performance computing in order to solve new problems and generally tackle and research questions related to this line of work.


  1. Introduction to optimization
    1. Problem statement. Types and examples
    2. Basic optimization concepts
  2. Methods of optimization
    1. Optimization with and without constraints: traditional methods
    2. Heuristic optimization: algorithms based on ideas borrowed from natural processes: simulated annealing, evolutionary algorithms, immune networks, etc. Practical examples
  3. Application of optimization techniques to industrial problems

Recommended reading


  • Basic numerical calculus
  • Basic knowledge of computer architecture

Tuition language


Subject-Specific Competences

Code, description and proficiency level for each subject-specific competence
Code Competence Proficiency Level
CEM7 Evaluation and application of diverse mathematical and statistical theories, and available knowledge extraction and discovery processes, methods and techniques for large data volumes C
CEM8 Application of the theoretical and mathematical foundations of heterogeneous functions and data processing and analysis and evaluation and design of related methods for application in practical domains S

Learning Outcomes

Code, description and proficiency level for each subject learning outcome
Code Learning Outcome Associated competences Proficiency level
RA-APDI-1 Be familiar with examples of real applications and research trends and lines CEM7 A
RA-APDI-2 Select and apply optimization methods to specific problems CEM8 S
RA-APDI-3 Be familiar with the theory of classical optimization methods and heuristics CEM8 S
RA-APDI-7 To know, apply and be critic about the research bibliography so it can be used as a starting point for a research CEM7, CEM8 C
RA-APDI-12 Be familiar with examples of real applications and research trends and lines CEM7, CEM8 C
RA-APDI-12 Select and apply optimization methods to specific problems CEM7, CEM8 A
RA-APDI-103 Be familiar with and apply the optimization methods in production management CEM7, CEM8 S

Learning Guide

Learning Guide: Advanced Numerical Computation