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Tractable performance bounds for compressed sensing

Seminar | November 18 | 4-5 p.m. | 1011 Evans Hall

Speaker: Alexandre d'Aspremont, Assistant Professor, Department of Operations Research & Financial Engineering, Bendheim Center for Finance, Princeton University

Sponsor: Statistics, Department of

Recent results in compressed sensing show that, under certain
conditions, the sparsest solution to an underdetermined set of linear
equations can be recovered by solving a linear program. These results
either rely on computing sparse eigenvalues of the design matrix or on
properties of its nullspace. So far, no tractable algorithm is known
to test these conditions and most current results rely on asymptotic
properties of random matrices. Given a matrix A, we use semidefinite
relaxation techniques to test the nullspace property on A and show on
some numerical examples that these relaxation bounds can prove perfect
recovery of sparse solutions with relatively high cardinality.

Refreshments: Refreshments from about 3:45pm in 1011 Evans

Event Contact: 510-642-2781