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.
  • 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]