CDC 2016 workshop on solving large SDPs in control, machine learning, and robotics

This page is about a one-day workshop that Georgina and I organized on solving large-scale semidefinite programs in control, machine learning, and robotics at CDC 2016.
And here are the slides from the talks:
- Module 1: Fundamentals of semidefinite and SOS programming
  - Pablo Parrilo (MIT), Sum of squares techniques and polynomial optimization pdf]
  - Jean B. Lasserre (LAAS-CNRS), The moment-SOS approach in and outside optimization
- Module 2: SDP and scalability
  - Approximating SDPs with simpler optimization problems
    - Amir Ali Ahmadi (Princeton), DSOS and SDSOS Optimization
    - Georgina Hall (Princeton), Iterative LP and SOCP-based approximations to SDPs
  - Exploiting structure in SDPs
    - Pablo Parrilo (MIT), Dimension reduction for semidefinite programmimg
    - Antonis Papachristodoulou (Oxford), Exploiting chordal sparsity for analysis and design of large-scale networked systems
  - Better algorithms for SDPs
    - Defeng Sun (NUS), A two-phase augmented Lagrangian approach for linear and convex quadratic SDPs
    - Robert Freund (MIT), An extended Frank-Wolfe method with applications to low-rank matrix completion
- Module 3: Applications to control, machine learning, and robotics