CPD Computation via Recursive Eigenspace Decompositions. - Eric Evert; Michiel Vandecappelle; Lieven De Lathauwer (ICASSP, 2022)

Guarantees for Existence of a Best Canonical Polyadic Approximation of a Noisy Low-Rank Tensor. - Eric Evert; Lieven De Lathauwer (SIAM J. Matrix Anal. Appl., 2022)

A Recursive Eigenspace Computation for the Canonical Polyadic Decomposition. - Eric Evert; Michiel Vandecappelle; Lieven De Lathauwer (SIAM J. Matrix Anal. Appl., 2022)

Canonical Polyadic Decomposition via the Generalized Schur Decomposition. - Eric Evert; Michiel Vandecappelle; Lieven De Lathauwer (IEEE Signal Process. Lett., 2022)

Uniqueness Result and Algebraic Algorithm for Decomposition into Multilinear Rank- $(M_{r}, N_{r}, cdot)$ Terms and Joint Block Diagonalization. - Eric Evert; Nico Vervliet; Ignat Domanov; Lieven De Lathauwer (CAMSAP, 2023)

On Best Low Rank Approximation of Positive Definite Tensors. - Eric Evert; Lieven De Lathauwer (SIAM J. Matrix Anal. Appl., 2023)

Empirical Properties of Optima in Free Semidefinite Programs. - Eric Evert; Yi Fu; J. William Helton; John Yin (Exp. Math., 2023)

New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries. - Aditya Bhaskara; Eric Evert; Vaidehi Srinivas; Aravindan Vijayaraghavan (STOC, 2024)

Decomposition of a Tensor into Multilinear Rank- ({(M_{{r}},N_{{r}}, cdot )} ) Terms. - Ignat Domanov; Nico Vervliet; Eric Evert; Lieven De Lathauwer (SIAM J. Matrix Anal. Appl., 2024)

Matrix extreme points and free extreme points of free spectrahedra. - Aidan Epperly; Eric Evert; J. William Helton; Igor Klep (Optim. Methods Softw., 2024)