Difeng Cai, Emory University, USA

The past few years have seen the advent of big data, which brings unprecedented convenience to our daily life. Meanwhile, from a computational point of view, a central question arises amid the exploding amount of data: how to tame big data in an economic and efficient way. In the context of matrix computations, the question consists in the ability to handle large dense matrices. In this talk, I will first introduce data-sparse hierarchical representations for dense matrices. Then I will present recent development of a new data-driven algorithm called SMASH. The new method not only outperforms existing algorithms like the fast multipole method(FMM) but also works in high dimensions. Theory and experiments will be provided to justify the advantages of the new method.