Saturday Morning Videos : Semidefinite Optimization, Approximation and Applications (Simons Institute @ Berkeley)

I will come back to some of the videos and slides of this workshop on Semidefinite Optimization, Approximation and Applications organized at the Simons Institute at Berkeley (is Simons in the audience ? ). But in the meantime here are the links to pages that generally features both the slides and the videos (and sometimes none of the two).

• Analysis and Design of Optimization Algorithms via Integral Quadratic Constraints  Benjamin Recht, UC Berkeley
• Adventures in Linear Algebra++ and Unsupervised Learning Sanjeev Arora, Princeton University
• Learning Overcomplete Latent Variable Models through Tensor Power Method  Rong Ge, Microsoft Research
• Robust PCA via Non-convex Methods: Provable Bounds  Animashree Anandkumar, UC Irvine
• Convex Relaxations for Recovering Simultaneously Structured Objects  Maryam Fazel, University of Washington
• The Edge of Tractability: Unique Games vs Sum of Squares  David Steurer, Cornell University
• Recovering Hidden Sparsity via Sum of Squares  Jonathan Kelner, Massachusetts Institute of Technology
• A Polynomial Time Algorithm for Lossy Population Recovery  Ankur Moitra, Massachusetts Institute of Technology
• Serialrank: Spectral Ranking using Seriation  Alexandre d'Aspremont, CNRS - École Normale Superieure Paris
• Relative Entropy Relaxations for Signomial Optimization  Venkat Chandrasekaran, California Institute of Technology
• Robust Sketching for Large-Scale Multi-Instance Conic Optimization  Laurent El Ghaoui, UC Berkeley
• Spectrahedra and their Shadows  Bernd Sturmfels, UC Berkeley
• Spectral Bounds and SDP Hierarchies for Geometric Packing Problems  Frank Vallentin, University of Cologne
• Positive Semidefinite Rank Rekha Thomas, University of Washington
• An SDP-based Algorithmic Cheeger Inequality for Vertex Expansion  Santosh Vempala, Georgia Institute of Technology
• Tensor Decompositions: Uniqueness and Smoothed Analysis  Moses Charikar, Princeton University
• Convex Optimization and Quantum Information  Aram Harrow, Massachusetts Institute of Technology
• Noncommutative Polynomial Optimization under Dimension Constraints  Miguel Navascués Cobo, Universitat Autònoma de Barcelona
• Faster SDP Hierarchy Solvers for Local Rounding Algorithms  Ali Sinop, Institute for Advanced Study, Princeton
• Convergence of SDP Hierarchies Using Kernel Based Methods: Moment Matrices  Stephanie Wehner, National University of Singapore
• Controlling Eigenvalues (at least in theory)  Adam Marcus, Yale University
• Efficient First-Order Methods for Linear (and Semidefinite and Hyperbolic) Programming James Renegar, Cornell University
• Primal-Dual Symmetric Interior-Point Methods: From SDP to Hyperbolic Cone Programming and Beyond  Levent Tunçel, University of Waterloo
• Effectivity Issues and Results for Hilbert's 17th Problem Marie-Françoise Roy, Universite de Rennes

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