 This page is about a oneday workshop that Georgina and I organized on solving largescale semidefinite programs in control, machine learning, and robotics at CDC 2016.
 The description of the workshop as well as the abstracts of all talks can be found here:
 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 (LAASCNRS), The momentSOS approach in and outside optimization [pdf]
 Module 2: SDP and scalability
 Approximating SDPs with simpler optimization problems
 Amir Ali Ahmadi (Princeton), DSOS and SDSOS Optimization [pdf]
 Georgina Hall (Princeton), Iterative LP and SOCPbased approximations to SDPs [pdf]
 Exploiting structure in SDPs
 Pablo Parrilo (MIT), Dimension reduction for semidefinite programmimg [pdf]
 Antonis Papachristodoulou (Oxford), Exploiting chordal sparsity for analysis and design of largescale networked systems [pdf]
 Better algorithms for SDPs
 Defeng Sun (NUS), A twophase augmented Lagrangian approach for linear and convex quadratic SDPs [pdf]
 Robert Freund (MIT), An extended FrankWolfe method with applications to lowrank matrix completion [pdf]
 Module 3: Applications to control, machine learning, and robotics
 Vikas Sindhwani (Google Brain), Geometric reasoning in 3D environments using SOS programming [pdf]
 Anirudha Majumdar (Stanford), Controlling agile robots with formal safety guarantees [pdf]
 Mario Sznaier (Northeastern), The interplay between sparsity and big data in systems theory [pdf]

