Explore >> Select a destination
|
You are here 
|
live-simons-blog.pantheonsite.io | ||
| | | | |
lucatrevisan.wordpress.com
|
|
| | | | | I am writing a short survey on connections between additive combinatorics and computer science for SIGACT News and I have been wondering about the "history" of the connections. (I will be writing as little as possible about history in the SIGACT article, because I don't have the time to research it carefully, but if readers... | |
| | | | |
scottaaronson.blog
|
|
| | | | | Way back in 2005, I posed Ten Semi-Grand Challenges for Quantum Computing Theory, on at least half of which I'd say there's been dramatic progress in the 16 years since (most of the challenges were open-ended, so that it's unclear when to count them as "solved"). I posed more open quantum complexity problems in 2010,... | |
| | | | |
windowsontheory.org
|
|
| | | | | (Also available as a pdf file. Apologies for the many footnotes, feel free to skip them.) Computational problems come in all different types and from all kinds of applications, arising from engineering as well the mathematical, natural, and social sciences, and involving abstractions such as graphs, strings, numbers, and more. The universe of potential algorithms... | |
| | | | |
nhigham.com
|
|
| | | The trace of an $latex n\times n$ matrix is the sum of its diagonal elements: $latex \mathrm{trace}(A) = \sum_{i=1}^n a_{ii}$. The trace is linear, that is, $latex \mathrm{trace}(A+B) = \mathrm{trace}(A) + \mathrm{trace}(B)$, and $latex \mathrm{trace}(A) = \mathrm{trace}(A^T)$. A key fact is that the trace is also the sum of the eigenvalues. The proof is by... | ||
