The project should involve at least one of the following components.

  • Theory: A new theoretical result involving convex optimization and related applications, e.g. Quality of an SDP relaxation of a specific combinatorial problem.

  • Literature review: A report on a set of papers dealing with the topic, e.g. Literature review of robust optimization with recourse in control.

  • Algorithms: A comparative study of different convex optimization algorithms, possibly including your own, e.g. Study of algorithms for the LASSO problem.

  • Application: An application of convex optimization models and an evaluation on real or simulated data, e.g. Robust optimization for image deformation.

  • Data analysis: An analysis of a dataset, making extensive use of convex optimization, e.g. Analysis of airport weather data via sparse PCA.

The project deliverables is a .zip file that includes

  • a 5-10 page report written in jemdoc syntax;

  • codes and documentation;

  • pdf files of papers relevant to the project.