Dynamical low-rank approximation is a differential-equation based approach to efficiently computing low-rank approximations to time-dependent large data matrices or to solutions o...
For the approximation of time-dependent data tensors and of solutions to tensor differential equations by tensors of low Tucker rank, we study a computational approach that can be ...
Iceberg-cube mining is to compute the GROUP BY partitions, for all GROUP BY dimension lists, that satisfy a given aggregate constraint. Previous works have pushed anti-monotone co...
Ke Wang, Yuelong Jiang, Jeffrey Xu Yu, Guozhu Dong...
The role of space is more and more accepted as a way to dramatically improve the success of coevolutionary function approximation. The process behind this success however is not y...
We study the quality of LP-based approximation methods for pure combinatorial problems. We found that the quality of the LPrelaxation is a direct function of the underlying constra...