Timeline for Progressive discrete multifunction optimization
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 29, 2014 at 17:51 | vote | accept | PythonNut | ||
| Jul 27, 2014 at 20:37 | comment | added | D.W.♦ | @PythonNut, sure, you could add stochastic sampling as well if you wish. For instance, you could select a fraction $p$ of the functions in $F$, and only evaluate those functions (ignoring the rest). This could be made reproducible ("deterministic") by using a hash function from $F$ to $\{0,1\}$ that has a probability $p$ of being $1$ for any particular $f_i$. | |
| Jul 26, 2014 at 0:33 | comment | added | PythonNut | The only problem is that the running time of $\Phi$ could be very long (probably hundreds of hours, even at the start). Ultimately, I want $F$ to be as close to infinite in size as I can get. So there needs to be some element of stochastic sampling of $F$ as well. | |
| Jul 25, 2014 at 20:10 | history | answered | D.W.♦ | CC BY-SA 3.0 |