We introduce and study Bregman functions as objectives for non-negative sparse compressed sensing problems together with a related first-order iterative ...
We introduce and study Bregman functions as objectives for non-negative sparse compressed sensing problems together with a related first-order iterative scheme.
Abstract. We introduce and study Bregman functions as objectives for non-negative sparse compressed sensing problems together with a related first-order ...
B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems ; Publication Type, Conference Paper ; Year of Publication, 2013.
We introduce and study Bregman functions as objectives for non-negative sparse compressed sensing problems together with a related first-order iterative ...
B-SMART: Bregman-based first-order algorithms for non-negative compressed sensing problems. S Petra, C Schnörr, F Becker, F Lenzen.
... Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems, proceedings SSVM 2013, LNCS, vol. 7893, pp. 110-124 , Springer, 2013 [pdf] ...
Our approach is especially useful for many compressed sensing applications where matrix-vector operations involving A and A> can be computed by fast transforms.
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The main purpose of this paper is to show that the Bregman iterative procedure is a simple but very efficient method for solving the basis pursuit problem (1.1) ...
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Our approach is especially useful for many compressed sensing applications where matrix-vector operations involving A and A ⊤ can be computed by fast ...