Optimization Techniques 2019

EVANGELOS MARKAKIS - STAVROS TOUMPIS - GEORGIOS ZOIS

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The course provides an overview of optimization tools and techniques, motivated by large-scale problems that arise in Data Science. Optimization is at the heart of various critical tasks related to handling big data (e.g. classification problems, machine learning, discrete optimization, etc), and a great number of methodologies have been developed over the years. The course aims at first to illustrate how we can model problems as optimization questions. We will then examine a variety of techniques that are currently used for solving such problems in practice (including among others, linear programming and convex programming techniques, combinatorial algorithms, local search methods and genetic algorithms).

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