27th Conference of the International Linear Algebra Society (ILAS 2026)

Session

Sparse Tensor Computations: Algorithms and Applications

MS 31

May 18, 2026, 11:00 AM

311. Optimal tensor network structure search

Alex Gorodetsky (University of Michigan)

5/18/26, 11:00 AM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

Tensor networks provide a powerful framework for compressing multi-dimensional data. The optimal tensor network structure for a given data tensor depends on both data characteristics and specific optimality criteria, making tensor network structure search a difficult problem. Existing solutions typically rely on sampling and compressing numerous candidate structures; these procedures are...

438. Decomposition of a tensor into multilinear rank-$(M_r,N_r,\cdot)$ terms

Nico Vervliet (KU Leuven)

5/18/26, 11:25 AM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

We present new generic and deterministic uniqueness results for block term decompositions (BTD). These uniqueness conditions hold under mild assumptions and apply to more general settings than previously known results. We also present an algebraic algorithm for the computation of BTDs. Our algorithm requires no knowledge of the block sizes appearing in the BTD: these block sizes are recovered...

115. Synchronous and Asynchronous Parallelism Approaches for Generalized Canonical Polyadic Tensor Decomposition with GenTen

Eric Phipps (Sandia National Laboratories)

5/18/26, 11:50 AM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

The Canonical Polyadic (CP) tensor decomposition is a well-known method for interpretable analysis of high-dimensional data.  Recently, the Generalized CP (GCP) method was introduced by Hong, Kolda and Duersch (2020) to allow for flexible choice of the loss function in the optimization problem defining the CP model, enabling more interpretable decompositions of strongly non-Gaussian data such...

198. A Tree Tensor Network Integrator for the Chemical Master Equation

Julian Mangott (Universität Innsbruck)

5/18/26, 2:00 PM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

The development of new drugs and therapies increasingly relies on the numerical simulation of the reaction networks inside biological cells. However, the most accurate description of such reaction networks with the chemical master equation (CME) suffers from the curse of dimensionality, meaning that memory and computational cost grow exponentially with the number of dimensions. This renders...

379. Low-Rank CP Tensor Compression and Its Application to High-Dimensional PDEs

Zhanrui Zhang (University of Illinois Urbana-Champaign)

5/18/26, 2:25 PM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

The Canonical Polyadic (CP) decomposition is widely used to represent high-dimensional data in many applications, for example, solving high-dimensional PDEs like kinetic equations. A key challenge in these problems is the efficient estimation and reduction of the CP rank. The CP rank reduction task can be formulated as approximating the Khatri–Rao product of the CP factor matrices with a lower...

125. Iterative low-rank time integration of the time-dependent Schrödinger equation

Polina Sachsenmaier (RWTH Aachen University)

5/18/26, 2:50 PM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

Standard numerical methods for solving PDEs typically suffer from the curse of dimensionality: their computational cost scales exponentially with the dimension of the underlying domain, making them impractical even at low resolution. In many cases of interest, however, such limitations can be overcome by appropriately compressed representations of approximate solutions, for example, by...

190. Efficient oversampled Tensor-Train approximations

Daniel Hayes (University of Delaware)

5/19/26, 11:00 AM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

Recently, there have been many advances in the area of randomized and sampling-based methods for data approximation. This has led to significant progress towards the efficient treatment of large data in both compression and utilization in computation. In this talk, I will discuss a current work that uses random oversampling on a Tensor Train Cross (TT-Cross) approximation in order to reduce...

314. Adaptive Randomized Tensor Train Rounding using Khatri-Rao Products

Bhisham Dev Verma (Wake Forest University)

5/19/26, 11:25 AM

Sparse Tensor Computations: Algorithms and Applications

Minisymposium Talk

The Tensor Train (TT) format provides a compact and scalable way to represent high-dimensional tensors, making it essential for solving certain parametrized partial differential equations and other large-scale problems. A critical operation in TT-based computations is rounding, which reduces the ranks of a tensor in TT format to maintain efficiency. While recent randomized rounding algorithms...