Name
Abstract | ||
|---|---|---|
This paper describes solvers for specific quadratic programming (QP) tasks. The QP tasks in question appear in numerous problems, e.g., classifier learning and probability density estimation. The QP task becomes challenging when large number of variables is to be optimized. This the case common in practice. We propose QP solvers which are simple to implement and still able to cope with problems having hundred thousands variables. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550518_10 | DAGM-Symposium |
Keywords | Field | DocType |
hundred thousands variable,case common,specific quadratic programming,numerous problem,probability density estimation,simple solvers,large number,qp task,qp solvers,classifier learning,large quadratic programming task,probability density,quadratic program | Density estimation,Line segment,Mathematical optimization,Probability density estimation,Computer science,Support vector machine,Quadratic programming,Classifier (linguistics),Sequential algorithm | Conference |
Volume | ISSN | ISBN |
3663 | 0302-9743 | 3-540-28703-5 |
Citations | PageRank | References |
5 | 0.54 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vojtěch Franc | 1 | 584 | 55.78 |
| Václav Hlaváč | 2 | 216 | 13.42 |