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Sharpness, Restart and Compressed Sensing Performance

Presented by: 
Alex d'Aspremont CNRS - Ecole Normale Superieure Paris
Date: 
Wednesday 17th January 2018 - 09:00 to 09:45
Venue: 
INI Seminar Room 1
Abstract: 
Joint work with Vincent Roulet (University of Washington) and Nicolas Boumal (Princeton University). We show that several classical quantities controlling compressed sensing performance directly match parameters controlling algorithmic complexity. We first describe linearly convergent restart schemes on first-order methods using a sharpness assumption. The Lojasievicz inequality shows that sharpness bounds on the minimum of convex optimization problems hold almost generically. Here, we show that sharpness directly controls the performance of restart schemes. For sparse recovery problems, sharpness at the optimum can be written as a condition number, given by the ratio between true signal sparsity and the largest signal size that can be recovered by the observation matrix. Overall, this means that in compressed sensing problems, a single parameter directly controls both computational complexity and recovery performance.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons