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| | nickhar.wordpress.com
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| | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B...
| | lucatrevisan.wordpress.com
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| | (This is the sixth in a series of posts on online optimization techniques and their ``applications'' to complexity theory, combinatorics and pseudorandomness. The plan for this series of posts is to alternate one post explaining a result from the theory of online convex optimization and one post explaining an ``application.'' The first two posts were...
| | leanprover-community.github.io
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| | A few weeks ago, we announced the completion of the liquid tensor experiment (LTE for short). What this means is that we stated and (completely) proved the following result in Lean: variables (p' p :
| | humanwhocodes.com
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| The Official Web Site of Nicholas C. Zakas