T
Tamara G. Kolda
Researcher at Sandia National Laboratories
Publications - 175
Citations - 25147
Tamara G. Kolda is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Tensor & Symmetric tensor. The author has an hindex of 56, co-authored 171 publications receiving 21859 citations. Previous affiliations of Tamara G. Kolda include College of William & Mary & Office of Scientific and Technical Information.
Papers
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Journal ArticleDOI
Tensor Decompositions and Applications
Tamara G. Kolda,Brett W. Bader +1 more
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.
Journal ArticleDOI
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods ∗
TL;DR: This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited, then turns to a broad class of methods for which the underlying principles allow general-ization to handle bound constraints and linear constraints.
Journal ArticleDOI
An overview of the Trilinos project
Michael A. Heroux,Roscoe A. Bartlett,Vicki E. Howle,Robert J. Hoekstra,Jonathan Joseph Hu,Tamara G. Kolda,Richard B. Lehoucq,Kevin Long,Roger P. Pawlowski,Eric T. Phipps,Andrew G. Salinger,Heidi K. Thornquist,Raymond S. Tuminaro,James M. Willenbring,Alan B. Williams,Kendall S. Stanley +15 more
TL;DR: The overall Trilinos design is presented, describing the use of abstract interfaces and default concrete implementations and how packages can be combined to rapidly develop new algorithms.
Journal ArticleDOI
Scalable tensor factorizations for incomplete data
TL;DR: An algorithm called CP-WOPT (CP Weighted OPTimization) that uses a first-order optimization approach to solve the weighted least squares problem and is shown to successfully factorize tensors with noise and up to 99% missing data.
Journal ArticleDOI
Efficient MATLAB Computations with Sparse and Factored Tensors
Brett W. Bader,Tamara G. Kolda +1 more
TL;DR: This paper considers how specially structured tensors allow for efficient storage and computation, and proposes storing sparse tensors using coordinate format and describes the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms.