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"Optimization Models and Applications"

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Course Materials Include:

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Optimization Models and Applications

By Professor Laurent El Ghaoui
Electrical Engineering
University of California, Berkeley

This course offers an introduction to optimization models, with emphasis on numerically tractable problems.

The course starts by introducing basic linear algebra, and then focuses on convex models (Convexity, LP, QP, SOCP, Robust LP, GP, and SDP are all introduced and explained). Non-convex models are also briefly introduced.

An overview on Duality is also given, and the course ends with four case studies such as Senate Voting, Antenna Arrays, Localization, and Circuit Design.

Learning Outcomes

  • Ability to quickly provide experimental data analysis results involving large-scale linear algebra skills pertaining to the use of principal component analysis, factor analysis, and eigenvalue decomposition of symmetric matrices.
  • Ability to master optimization models in practice through the use of state-of-the-art prototyping software, such as CVX, with real-life data sets.
  • Familiarity with a substantial number of concrete engineering applications, ranging from intrusion detection in networks, to circuit or filter design, to the analysis of text documents, or genetic databases.
  • Familiarity with basic statistical problems arising in the analysis of large-scale data sets.
  • Familiarity with the main theoretical tools and concepts of convex optimization.