Optimization models and methods II

linma2471  2021-2022  Louvain-la-Neuve

Optimization models and methods II
5.00 credits
30.0 h + 22.5 h
Q1
Teacher(s)
Glineur François; Nunes Grapiglia Geovani;
Language
English
Prerequisites
A basic optimization course (such as LINMA1702) and basic knowledge in real analysis and linear algebra (such as provided by FSAB1101 and FSAB1102)
Main themes
Linear optimization, convex optimization (including structured conic optimization) ; duality and applications ; interior-point methods ; first-order methods ; trust-region methods ; use of a modeling language.
Learning outcomes

At the end of this learning unit, the student is able to :

1 Learning outcomes:
AA1.1, AA1.2, AA1.3
AA2.1, AA2.2, AA2.4, AA2.5
AA5.3, AA5.5

More specifically, at the end of the course the student will be able to :
  • recognize the possiblity of formulating or converting a problem into a linear, convex or conic optimization program
  • exploit the concept of duality in order to understand a problem, produce optimality or impossibility certificates, carry out sensitivity analysis or formulate robust problems
  • describe, analyze and implement advanced algorithms to solve linear, convex or non-linear optimization problems
  • use a modeling language to formulate and solve optimization problems, while understanding and exploiting the formal separation between model, data and resolution algorithm
Transversal learning outcomes :
  • use a numerical/computational software tool such as MATLAB, or a modeling language such as AMPL
  • formulate, analyze and solve optimization models, in a small group
  • write a report about the formulation, analysis and resolution of optimization models, in a small group
 
Content
Models: Advanced modeling techniques for linear and convex optimization ; structured conic optimization ; convex duality with applications (alternatives, sensitivity analysis and robust optimization) ; Lagrangian duality
Methods: path-following interior-point methods for convex optimization (self-concordant barriers) ; first-order methods for convex and non-convex optimization (including stochastic methods) ; algorithmic complexity and convergence rates ; introduction to the AMPL modeling language.
Applications in various domains, such as data analysis, machine learning, finance, shape or structural  optimization (mechanics), telecommunications, etc.
Teaching methods
The course is comprised of lectures, exercise sessions and computer labs, as well as a series of homework assignments to be carried out in small groups.
Evaluation methods
Students will be evaluated with an individual written exam, based on the above-mentioned learning outcomes. Students also carry out a series of homework assignments in small groups during the first quadrimester, which are taken into account for the final grade (40%) for each session.
Online resources
https://moodle.uclouvain.be/course/view.php?id=1415
Bibliography
  • Convex Optimization, Stephen Boyd et Lieven Vandenberghe, Cambridge University Press, 2004.
  • Lectures on Modern Convex Optimization: Analysis, Algorithms, and Engineering Applications, Aharon Ben-Tal, Arkadi Nemirovski, SIAM 2001.
  • Interior point methods for linear optimization, Cornelis Roos, Tamas Terlaky, Jean-Philippe Vial, Springer, 2006.
  • Introductory Lectures on Convex Optimization: A Basic Course, Yurii Nesterov, Kluwer, 2004.
  • Lectures on Convex Optimization, Y. Nesterov, Springer, 2018
Faculty or entity
MAP


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Mathematics
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5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Data Science: Information Technology
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Master [120] in Biomedical Engineering
GBIO2M
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Master [120] in Computer Science
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5
Master [120] in Mathematical Engineering
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5