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UCLA Applied Math Research Paper Recognized as One of Most-Cited

“The Split Bregman Method for L1-Regularized Problems” (SIAM J. Imaging Sci. 2[2]: 323-43, 2009) authored by UCLA Math Professor Stanley Osher and then PhD student Tom Goldstein has been identified by Thomson Reuters Essential Science IndicatorsSM as a featured New Hot Paper in the field of computer science. The distinction has been given to the research article as one of the most-cited papers in this discipline published during the past two years. The class of L1-regularized optimization problems has received much attention because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. Osher and Goldstein show that the Bregman iteration can be used to solve rapidly and accurately a wide variety of constrained optimization problems, such as image denoising and a compressed sensing problem that arises in magnetic resonance imaging and elsewhere.

Click Here to read the abstract and download the full paper

Click Here to read an interview with Goldstein and Osher about the paper