Checking date: 14/03/2024

Course: 2024/2025

Optimization for large-scale data
(17235)
Master in Big Data Analytics (Plan: 352 - Estudio: 322)
EPI

Coordinating teacher: RUIZ MORA, CARLOS

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:

Objectives
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. 4. To develop methods and techniques that can be used to improve the efficiency and effectiveness of processes. In the context of the SDG associated with sustainability and climate change, optimization plays a crucial role in addressing issues related to resource management, emission reduction, sustainable supply chain planning, among other aspects.
Skills and learning outcomes
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

Calendar of Continuous assessment

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
Electronic Resources *
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

The course syllabus may change due academic events or other reasons.