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Sparse Model Recovery via Iterative Algorithms

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Abstract: In this talk, I shall discuss design of simple, iterative algorithms to recover an n-dimensional nonnegative-valued vector x from an m-dimensional nonnegative vector y = Ax, with A being an m x n matrix having 0/1 entries. Interest is in the scenario when m << n and the goal is to discuss conditions on A under which the algorithm will be able to recover x successfully. Specifically, I will discuss two sets of conditions : (a) known conditions regarding expansion of the matrix A, and (b) a new `witness` condition with application to learning rankings. The talk is based on joint works with (a) V. Chandar and G. Wornell,(b) S. Jagabathula—all at MIT .

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