|
You are here |
mycqstate.wordpress.com | ||
| | | | |
nickhar.wordpress.com
|
|
| | | | | 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
|
|
| | | | | (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
|
|
| | | | | 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
|
|
| | | The Official Web Site of Nicholas C. Zakas | ||