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Jocelyn Chi (University of Minnesota Twin Cities)5/21/26, 2:00 PMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
The canonical polyadic (CP) tensor decomposition represents a multidimensional data array as a sum of rank-one outer products of latent factors. Building on CP-HiFi, the hybrid infinite- and finite-dimensional CP framework of Larsen et al. (2024), which introduces quasitensors by modeling selected modes as smooth functional factors in a reproducing kernel Hilbert space, we replace the standard...
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Katherine Pearce (Oden Institute, UT Austin)5/21/26, 2:25 PMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
Attention mechanisms are a central component of transformer models that capture contextual relationships between tokens in large language models. Although many of the underlying computations (e.g., query, key, and value embeddings in multi-head attention) are inherently multi-way, classical transformer models are built on matrix-based formulations.
In this talk, we discuss several ways that...
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Dr Karl Pierce (University of Maryland, College Park)5/21/26, 2:50 PMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
The CANDECOMP/PARAFAC (CP) decomposition is a powerful tool used for multiway data analysis and to break the “curse of dimensionality” associated with higher-order tensors. The most common way to compute the CP decomposition of a tensor is via a standard alternating least squares (CP-ALS) algorithm. With the CP-ALS, one must iteratively solve a set of overdetermined least squares problem which...
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Zichao Wendy DI (Argonne National Lab)5/21/26, 3:15 PMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
Ptychography is a powerful coherent diffraction imaging technique essential for reconstructing high-resolution, complex-valued images from intensity-only measurements. However, the reconstruction poses significant challenges due to its nonconvex and ill-posed nature. We propose a novel multilevel optimization framework emphasizing stochastic learning principles to efficiently address these...
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Evan Coleman (University of Mary Washington)5/22/26, 8:45 AMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
Asynchronous iterative methods, such as Asynchronous Jacobi, offer a promising mechanism for overcoming synchronization bottlenecks in massively parallel and heterogeneous computing environments. By allowing processing elements to update components using the latest available data without global barriers, these methods maximize computational throughput. However, asynchrony also makes...
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Mitchell Scott (Emory University)5/22/26, 9:10 AMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
The column subset selection problem seeks to find a collection of the matrix columns that have similar spectral properties to the original matrix. Recently with the large amount of data available, many have turned to using randomization to reduce the problem's computation. While there have been many methods that motivate how to select these columns, they are just that--individual columns....
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Haixiao Wang (University of Wisconsin-Madison)5/22/26, 9:35 AMTopics in Randomized Numerical Linear AlgebraMinisymposium Talk
In modern machine learning applications, data matrices are always assumed to admit the signal-plus-noise structure. Typically, we assume that the spectra of signal and noise matrices are well-separated and that noise subspaces only produce a marginal influence. While these assumptions are readily verified for dense matrices via classical random matrix theory, real-world data is often sparse,...
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