List your company

Sparse Linear Regression on Simulated Sparse Linear Regression Instances

0Relative Upper Bound DifferenceGoemans-Williamson rounding
-1.367.821726.18Oct 20, 2015

Evaluation results

MethodLinks
Goemans-Williamson roundinglambda=0.1, mu=0.52015.100
Goemans-Williamson roundinglambda=0.2, mu=0.52015.100
Goemans-Williamson roundinglambda=0.3, mu=0.42015.100
Goemans-Williamson roundinglambda=0.3, mu=0.52015.100
Goemans-Williamson roundinglambda=0.4, mu=0.52015.100
Goemans-Williamson roundinglambda=0.5, mu=0.42015.100
Goemans-Williamson roundinglambda=0.5, mu=0.52015.100
Goemans-Williamson roundinglambda=0.1, mu=0.42015.101
Goemans-Williamson roundinglambda=0.2, mu=0.42015.101
Goemans-Williamson roundinglambda=0.4, mu=0.42015.101
Goemans-Williamson roundinglambda=0.1, mu=0.32015.102
Goemans-Williamson roundinglambda=0.2, mu=0.32015.102
Goemans-Williamson roundinglambda=0.4, mu=0.22015.103
Goemans-Williamson roundinglambda=0.4, mu=0.32015.103
Goemans-Williamson roundinglambda=0.5, mu=0.32015.103
Goemans-Williamson roundinglambda=0.3, mu=0.32015.106
Goemans-Williamson roundinglambda=0.1, mu=0.22015.107
Goemans-Williamson roundinglambda=0.5, mu=0.22015.109
Goemans-Williamson roundinglambda=0.2, mu=0.22015.1013
Goemans-Williamson roundinglambda=0.3, mu=0.22015.1014
Goemans-Williamson roundinglambda=0.5, mu=0.12015.1020
Goemans-Williamson roundinglambda=0.1, mu=0.12015.1021
Goemans-Williamson roundinglambda=0.2, mu=0.12015.1028
Goemans-Williamson roundinglambda=0.3, mu=0.12015.1030
Goemans-Williamson roundinglambda=0.4, mu=0.12015.1034