| dc.contributor |
European Commission |
|
| dc.contributor |
Tang, Junqi |
|
| dc.creator |
Tang, Junqi |
|
| dc.date |
2019-01-28T16:28:04Z |
|
| dc.date |
2019-01-28T16:28:04Z |
|
| dc.date.accessioned |
2023-02-17T20:53:29Z |
|
| dc.date.available |
2023-02-17T20:53:29Z |
|
| dc.identifier |
Tang, Junqi. (2019). Structure-Adaptive Large-Scale Convex Optimization Toolbox v1.0, [software]. University of Edinburgh. Institute for Digital Communications. https://doi.org/10.7488/ds/2489. |
|
| dc.identifier |
https://hdl.handle.net/10283/3249 |
|
| dc.identifier |
https://doi.org/10.7488/ds/2489 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/244119 |
|
| dc.description |
This toolbox includes the Matlab implementations of the GPIS and Acc-GPIS algorithms for efficiently solving the l_1 constrained least-squares regression and nuclear-norm constrained multivariate regression tasks, proposed in the ICML 2017 paper "Gradient Projection Iterative Sketch for Large-Scale Constrained Least-Squares", as well as the Rest-Katyusha and Adaptive Rest-Katyusha algorithms for the Lasso and elastic-net regularized least-squares regression tasks, proposed in the NeurIPS 2018 paper "Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes". |
|
| dc.format |
application/zip |
|
| dc.language |
eng |
|
| dc.publisher |
University of Edinburgh. Institute for Digital Communications |
|
| dc.relation |
http://proceedings.mlr.press/v70/tang17a.html |
|
| dc.relation |
http://papers.nips.cc/paper/7325-rest-katyusha-exploiting-the-solutions-structure-via-scheduled-restart-schemes |
|
| dc.rights |
Creative Commons Attribution 4.0 International Public License |
|
| dc.subject |
Optimization |
|
| dc.subject |
Machine Learning |
|
| dc.subject |
Big Data |
|
| dc.subject |
Mathematical and Computer Sciences::Machine Learning |
|
| dc.title |
Structure-Adaptive Large-Scale Convex Optimization Toolbox v1.0 |
|
| dc.type |
software |
|