Checking date: 29/06/2020

Course: 2020/2021

Optimization for large-scale data
Study: Master in Big Data Analytics (322)

Coordinating teacher: RUIZ MORA, CARLOS

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS


Competences and skills that will be acquired and learning results.
The goal of this course is to become familiar with the modeling and the application of optimization methods in complex decision-making processes. In this way, we provide the necessary tools and modern techniques of optimization for the efficient solution of many decision-making problems arising in diverse areas like Business, Marketing, Finance and Engineering. In particular, the objectives are: 1. Modeling and application of optimization methods for a series of general problems (linear models, discrete models, nonlinear models and also optimization under uncertainty) 2. Learn about the basic (mathematical) foundations that support the development of solution algorithms for the optimization problems mentioned above 3. Use Python to apply tools of modern optimization techniques in an efficient way.
Description of contents: programme
1. Linear Models 1.1 Examples 1.2 Properties 1.3 Algorithms 2. Discrete Models 2.1 Introduction 2.2 Logic conditions 2.3 Networks 2.4 Algorithms 3. Nonlinear Models 3.1 Examples, least squares 3.2 Optimality conditions 3.3 Algorithms 4. Uncertainty Models 4.1 Introduction and properties 4.2 Stochastic Optimization
Learning activities and methodology
½ lectures with supporting materials available on the Web ½ practical sessions (computer labs with Python)
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Basic Bibliography
  • Bertsimas, Dimitris, and John Tsitsiklis. Introduction to Linear Optimization. Belmont, MA: Athena Scientific. 1997
  • D Bertsimas, R Weismantel. Optimization over integers. Belmont: Dynamic Ideas. 2005
  • Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press. 2004
Recursos electrónicosElectronic Resources *
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The course syllabus and the academic weekly planning may change due academic events or other reasons.