- 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.
- 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 (LAAS-CNRS), The moment-SOS 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 SOCP-based 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 large-scale networked systems [pdf]
- Better algorithms for SDPs
- Defeng Sun (NUS), A two-phase augmented Lagrangian approach for linear and convex quadratic SDPs [pdf]
- Robert Freund (MIT), An extended Frank-Wolfe method with applications to low-rank 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]
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