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Sep 13, 2015 at 20:41 comment added Suvrit One minor quip: actually, 1M is not "big" these days :-) One has to have several 100M as dimension, or better yet, in the billions for it to be called "big" these days.
Sep 13, 2015 at 13:55 vote accept Manfred Weis
Sep 13, 2015 at 13:48 comment added Suvrit @ManfredWeis In "big data" computations, the focus is less on "high precision", because the computations are often done of very noisy data, and trying to obtain very high precision solutions is science fiction. However, the core ideas of "conditioning", "stability", "efficient numerical linear algebra" all are not only present but also crucial to the success of algorithms and software for dealing with large-scale data intensive tasks (e.g., in optimization, machine learning, data mining, etc.)
Sep 13, 2015 at 12:43 history made wiki Post Made Community Wiki by Todd Trimble
Sep 13, 2015 at 12:04 comment added Federico Poloni A good way to get a feeling for what is going on could be browsing through the abstracts of a big conference in the field (for numerical linear algebra, see for instance this one, or checking out an issue of a review journal such as SIREV (siam.org/journals/sirev.php, unfortunately paywalled).
Sep 13, 2015 at 11:50 comment added Federico Poloni @ManfredWeis Both. An excellent example of a new big research field is compressed sensing. But people are also still doing research on challenging classical problems like the global convergence of the Newton method on polynomials.
Sep 13, 2015 at 11:31 comment added Manfred Weis Especially the big-data issue seems to be a new challenge; are the research efforts for finding more stable algorithms focused on "classical" tasks like integration or solving differential equations or are there also relatively new problems to be addressed?
Sep 13, 2015 at 11:08 history answered Federico Poloni CC BY-SA 3.0