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

Session

Low-Complexity Data-driven or Classical Algorithms and Applications

MS 23

May 18, 2026, 11:00 AM

186. Convergence of Randomized and Greedy Block Gauss-Seidel methods, as well as Asynchronous Iterations

Daniel Szyld (Temple University)

5/18/26, 11:00 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

We extend results known for the randomized (point and block)
Gauss-Seidel and the Gauss-Southwell methods for the case of a Hermitian and positive definite matrix to certain classes of non-Hermitian matrices. We consider cases with overlapping variables (as in Domain Decomposition). We obtain convergence results for a whole range of parameters describing the probabilities in the randomized...

193. Randomized Sketching for Tucker Tensors: From Compressed Summation to GMRES

Mirjeta Pasha

5/18/26, 11:25 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

Tensor equations of the form $L(X)=B$, with $L$ a sum of Kronecker products, arise across scientific computing, from high-dimensional PDEs to large-scale inverse problems. When the tensor order is moderate but mode sizes are very large, the Tucker format is an attractive choice — yet using it inside iterative solvers is notoriously hard, as operator applications, orthogonalization, and linear...

261. Novel Randomized Tensor-Train Sketch and Applications

Paul Cazeaux (Virginia Tech)

5/18/26, 11:50 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

The Tensor-Train (TT) or Matrix-Product States (MPS) format provides a compact, low-rank representation for high-dimensional tensors, widely used in many-body quantum physics and quantum chemistry. Its efficiency relies on rounding, which reduces tensor ranks to maintain feasible computational costs.
In this talk, we introduce a novel block-structured randomized sketch exploiting the TT...

262. Optimal Experimental Design for Gaussian Processes via Column Subset Selection

Jessie Chen (North Carolina State University)

5/18/26, 2:00 PM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

Gaussian process regression uses data measured at sensor locations to reconstruct a spatially dependent function with quantified uncertainty. However, if only a limited number of sensors can be deployed, it is important to determine how to optimally place the sensors to minimize a measure of the uncertainty in the reconstruction. We consider the Bayesian D-optimal criterion to determine the...

287. Accelerating Spectral Clustering of Time Series by approximating the Similarity Matrix using Randomly Pivoted Cholesky

Raf Vandebril (Dept. Computer Science, KU Leuven)

5/18/26, 2:25 PM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

A major problem for time series clustering is that computing the similarity matrix for the most used similarity measures becomes infeasible if number amount or length of time series becomes too large. However, since this similarity matrix typically has low-rank structure, it can be approximated using a low-rank approximation. In this work, we show that existing numerical linear algebra...

378. Spotlight inversion by orthogonal projections

Daniela Calvetti (Case Western Reserve University)

5/18/26, 2:50 PM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

Many computational problems involve solving a linear system of equations, although only a subset of the entries of the solution are needed. In inverse problems, where the goal is to estimate unknown parameters from indirect noisy observations, it is not uncommon that the forward model linking the observed variables to the unknowns depends on variables that are not of primary interest, often...

365. Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data

Dr Xianqi Li (Florida Institute of Technology)

5/19/26, 11:00 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive clinical scenarios. While undersampling strategies can accelerate image acquisition, they often result in image artifacts and degraded quality. Recent diffusion models have shown promise for reconstructing...

389. A low-complexity locally recoverable matrix-influenced algorithm to globally recover codes

Oshani Jayawardane (Embry-Riddle Aeronautical University)

5/19/26, 11:25 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

Code Recovery using algebraic-geometric approaches becomes computationally expensive with the cardinality of the field and the complexity of the code structures. In response, we present a low-complexity algorithm that utilizes structures in algebraic-geometric codes over finite fields. The low-complexity algorithm recovers algebraic codes over finite fields locally, which we name as lrc...

419. Computing Singular Values Above a Certain Threshold

Vasilije Perovic (University of Rhode Island)

5/19/26, 11:50 AM

Low-Complexity Data-driven or Classical Algorithms and Applications

Minisymposium Talk

In this talk we discuss various computational aspects of determining all singular triplets $\{\sigma_j, u_j, v_j\}$ corresponding to singular values of $A$ above a user-specified threshold parameter $sigma$, or in other words, determining a $k$-PSVD of $A$ such that $\sigma_k \geq sigma$ and $\sigma_{k+1} < sigma$. While various numerical schemes with publicly available software have been...