His research is on high performance and large scale matrix computations for analyzing data from social networks and scientific simulations.
The goal is to encourage distributed systems researchers to work on machine learning and numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities.
The goal is to encourage machine learning researchers to work on numerical linear algebra problems, to inform machine learning researchers about new developments on large scale matrix analysis, and to identify unique challenges and opportunities.
Large scale matrix-free finite element implementations save memory and are often significantly faster than implementations using classical sparse matrix techniques.
SDPARA can effectively solve SDPs with a large number of equality constraints; however, it does not solve SDPs with a large scale matrix variable with similar effectiveness.
For a (large scale) matrix of size n=m2k (m,k⩾1) and p=2q (⩽n/2) available processors, we first construct an adequate 2-phases task segmentation and inducing a balanced layered task graph.
In this paper, we present a low-rank approach which exploits the structure of the saddle point system using techniques and theory from solving large scale matrix equations.
SDPA-C is a primal dual interior-point method using the positive definite matrix completion technique by Fukuda et al., and it performs effectively with SDPs with a large scale matrix variable, but not with a large number of equality constraints.
Numerical experiments with the three parallel software packages SDPARA-C, SDPARA and PDSDP by Benson show that SDPARA-C efficiently solves SDPs with a large scale matrix variable as well as a large number of equality constraints with a small amount of memory.
({tilde{{mathbf{D}}}}) is a large scale matrix consisting of three reflectivity decomposition operators.
This method is accurate but time-intensive since it requires a large amount of computational resources, especially in the case of large scale matrix.
