20 citations · 20 across the 4 of their papers we have counts for
4 papers
Scalable symmetric Tucker tensor decomposition
Ruhui Jin, Joe Kileel, Tamara G. Kolda +1
We study the best low-rank Tucker decomposition of symmetric tensors. The motivating application is decomposing higher-order multivariate moments. Moment tensors have special struc…
Space-time reduced-order modeling for uncertainty quantification
Ruhui Jin, Francesco Rizzi, Eric Parish
This work focuses on the space-time reduced-order modeling (ROM) method for solving large-scale uncertainty quantification (UQ) problems with multiple random coefficients. In contr…
Tensor-structured sketching for constrained least squares
Ke Chen, Ruhui Jin
Constrained least squares problems arise in many applications. Their memory and computation costs are expensive in practice involving high-dimensional input data. We employ the so-…
Faster Johnson-Lindenstrauss Transforms via Kronecker Products
Ruhui Jin, Tamara G. Kolda, Rachel Ward
The Kronecker product is an important matrix operation with a wide range of applications in supporting fast linear transforms, including signal processing, graph theory, quantum co…