Optimization

• Teachers
• Weekly hours
• Competences
• Objectives
• Contents
• Activities
• Teaching methodology
• Evaluation methodology
• Bibliography
• Previous capacities

Teachers

Person in charge

• Pau Fonseca Casas ( )

Others

• Cecilio Angulo Bahon ( )
• Mari Paz Linares Herreros ( )

Weekly hours

Competences

Transversal Competences

Transversals

• CT2 [Avaluable] - Sustainability and Social Commitment. To know and understand the complexity of economic and social phenomena typical of the welfare society; Be able to relate well-being to globalization and sustainability; Achieve skills to use in a balanced and compatible way the technique, the technology, the economy and the sustainability.
• CT5 [Avaluable] - Solvent use of information resources. Manage the acquisition, structuring, analysis and visualization of data and information in the field of specialty and critically evaluate the results of such management.
• CT6 [Avaluable] - Autonomous Learning. Detect deficiencies in one's own knowledge and overcome them through critical reflection and the choice of the best action to extend this knowledge.

Basic

• CB2 - That the students know how to apply their knowledge to their work or vocation in a professional way and possess the skills that are usually demonstrated through the elaboration and defense of arguments and problem solving within their area of ??study.
• CB3 - That students have the ability to gather and interpret relevant data (usually within their area of ??study) to make judgments that include a reflection on relevant social, scientific or ethical issues.
• CB4 - That the students can transmit information, ideas, problems and solutions to a specialized and non-specialized public.

Technical Competences

Especifics

• CE01 - To be able to solve the mathematical problems that may arise in the field of artificial intelligence. Apply knowledge from: algebra, differential and integral calculus and numerical methods; statistics and optimization.
• CE20 - To select and put to use techniques of statistical modeling and data analysis, assessing the quality of the models, validating and interpreting.
• CE21 - To formulate and solve mathematical optimization problems.
• CE22 - To represent, design and analyze dynamic systems. To acquire concepts such as observability, stability and controllability.
• CE23 - To design controllers for dynamic systems that represent temporary physical phenomena in a real environment.

Generic Technical Competences

Generic

• CG2 - To use the fundamental knowledge and solid work methodologies acquired during the studies to adapt to the new technological scenarios of the future.
• CG4 - Reasoning, analyzing reality and designing algorithms and formulations that model it. To identify problems and construct valid algorithmic or mathematical solutions, eventually new, integrating the necessary multidisciplinary knowledge, evaluating different alternatives with a critical spirit, justifying the decisions taken, interpreting and synthesizing the results in the context of the application domain and establishing methodological generalizations based on specific applications.
• CG5 - Work in multidisciplinary teams and projects related to artificial intelligence and robotics, interacting fluently with engineers and professionals from other disciplines.
• CG8 - Perform an ethical exercise of the profession in all its facets, applying ethical criteria in the design of systems, algorithms, experiments, use of data, in accordance with the ethical systems recommended by national and international organizations, with special emphasis on security, robustness , privacy, transparency, traceability, prevention of bias (race, gender, religion, territory, etc.) and respect for human rights.
• CG9 - To face new challenges with a broad vision of the possibilities of a professional career in the field of Artificial Intelligence. Develop the activity applying quality criteria and continuous improvement, and act rigorously in professional development. Adapt to organizational or technological changes. Work in situations of lack of information and / or with time and / or resource restrictions.

Objectives

1. Be able to apply basic optimization techniques to be able to solve computationally complex problems.
   Related competences: CE23, CG4, CG5, CT5, CT6, CB2, CB3, CB4, CE01, CE21, CE22,
2. Contextualize the different existing optimization techniques.
   Related competences: CG2, CG5, CG8, CG9, CT2, CT5, CT6, CB2, CB3, CB4, CE20, CE21,

Contents

1. Introduction to optimization
   The concept and need for optimization will be presented. Examples and real cases will be shown in which some of the techniques that will be explained during the course have been used.
2. Discrete optimization
   Introducció a l'optimització discreta, dualitat, SIMPLEX...
3. Heuristics
   Optimització basada en heurístics.
4. Linear Dynamical Systems
   Introduction to linear dynamical systems and their representations: ordinal differential equations; Laplace transform; Fourier transform
5. Discrete Dynamical Systems Models
   Discrete representation of dynamical systems and modelling: AR, MA, ARMA, NARMAX
6. Control and Optimisation of Dynamical Systems
   Control of dynamical systems and optimisation processes for tuning

Activities

Activity Evaluation act

Teaching methodology

Evaluation methodology

Bibliography

Basic:

• Linear Programming - Robert J. Vanderbei, Springer New York Heidelberg Dordrecht London, 2014. ISBN: 978-1-4614-7629-0
  https://vanderbei.princeton.edu/LPbook/
• Numerical Optimization - Nocedal, Jorge; Wright, Stephen J., Springer, 2006. ISBN: 9780387303031
  https://discovery.upc.edu/discovery/fulldisplay?docid=alma991003178739706711&context=L&vid=34CSUC_UPC:VU1&lang=ca
• Essentials of Metaheuristics - Luke, Sean, [editor no identificat],, 2016. ISBN: 978-1-300-54962-8
  

Previous capacities