Timeline for Applications of basic linear algebra concepts to computer science?
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 10, 2019 at 2:27 | vote | accept | Kim | ||
| Feb 8, 2019 at 13:26 | comment | added | DreamConspiracy | ... but greedy $\epsilon$-approximations are relatively fast. | |
| Feb 8, 2019 at 13:25 | comment | added | DreamConspiracy | SVD are actually more widely used than this. They are used for many kinds of compression based on low rank approximation. Consider a case where we have $n$ data entries, each with $d$ dimensions, and we wish to compress this. If we believe that this data is "low rank"- i.e. the rank of the associated $d\crossn$ matrix is low, then the SVD truncated to $k$ values as described above gives a provably optimal low rank approximation. It turns out many modern data sets are low rank, so this is widely used. In terms of computational efficiency, computing an exact SVD is impossible, ... | |
| Feb 8, 2019 at 1:00 | comment | added | Somatic Custard | Yes, and more generally the linear algebra that arises from graph theory, via the adjacency matrix, graph Laplacian, etc. | |
| Feb 7, 2019 at 17:22 | history | answered | James | CC BY-SA 4.0 |