• Goals & deliverables

  • Topics & teams

  • Logistics

  • Timeline

  • Types of projects

  • Project proposals (in case you need suggestions)

Goal & deliverables

The deliverables consists in a single .zip file posted on bspace containing:

  • A 10-15 page project report detailing the case study. The report should be written preferably with LaTeX.

  • All the figures (in .pdf format) and matlab files needed to reproduce the results.

  • A README file detailing

    • The names and SID's of the students involved.

    • The contents of the .zip file (names and short description of each file).

  • The name of the .zip file should be of the form <XXX>, where XXX is a tag for the project. Example:


  • Each project involves a team of 3-4 students.

  • Groups and topics should be decided no later than October 28.

  • There will be a single project grade for each group.


  • By Sep. 16: teams are formed.

  • By October 28: topics are chosen.

  • By November 13: project mid-point review. You are to present an outline of your report and a progress status.

  • By December 5: final reports due, 10-11am, in 421 SDH.

  • December 5-12: project presentations, in class.

Types of projects

Projects can have the following different formats:

  • Literature review: The project describes in detail a set of 4-5 papers. This should include reproducing experiments. Example: learning sparse graphical models.

  • Design methodology: the project describes a (possibly new) methodology for addressing a particular design problem. Here the focus is not on algorithms or past work. Example: geometric programming for water distribution networks.

  • Theory: the project examines a theoretical issue related to convex optimization. Example: quality of SDP relaxation for sparse PCA.

  • Algorithms: the project tests various algorithms for solving a particular type of problem. Example: Stochastic vs. accelerated gradient for large-scale logistic regression.

Past project examples

Project ideas

See also the resources page on bCourses.

  • SPARSEPROB: this project deals with statistical estimation of sparse probabilities, in the context of low-rank data. Contact: Mert Pilanci (

  • ROBPRICE: this project involves the real-time pricing of millions of products sold online, based on uncertain demand. Contact: Arnau Puig (

  • UNBAL: This project involves the solution of large binary classification problems where the number of examples in one class is much smaller than in the other. Contact: Edouard Grave (

  • COVEST: This project involves the estimation of large covariance matrices with sparse inverses, using market data. Contact: Laurent El Ghaoui.

  • MATCOMP: This project involves matrix completion problems, more specifically an extension of the robust PCA paradigm (which aims at decomposing a data matrix as a sum of a sparse and a low-rank matrix), to the case when the data matrix has missing entries. Contact: Laurent El Ghaoui.

  • HYDRO: this project is concerned with the management of a hydro-electric production line, which has been already formulated as a linear program with uncertain coefficients. Here the uncertainty is related to the demand, which has to be predicted. The focus of the project is on so-called affine recourse policies, as a method to handle the uncertain components. Contact: Laurent El Ghaoui.

  • DCOPT: this project addresses a class of non-convex problems known as ‘‘difference of convex functions’’, which arise in a host of problems, including those involving the estimation of mixtures of probability distributions based on data. The project will examine: a) a review of the literature; b) a connection with existing algorithms, such as block-coordinate descent, Expectation-Maximization; and c) the development of innovative approaches, based for example on semi-definite programming relaxations. Contact: Jerome Thai (