activity
20202026
most citedAlgebraic degree of optimization over a variety with an application to -norm distance degree

2 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

math.AG2026

Nash Loci

Luca Sodomaco, Julian Weigert

In the study of Nash equilibria of finite-player games, one often seeks equilibria that are compatible with predetermined constraints, either determined by the players or by an ext…

math.HO2026

Benchmarks in Leipzig

Andrei Balakin, Miklós Bóna, Marie-Charlotte Brandenburg +45

Between April 1 and May 15, 2026, a group of 49 mathematicians compiled a dataset of research-level mathematics questions with known answers. Most of the work was done during the t…

math.AG2025

Nonlinear Kalman varieties

Flavio Salizzoni, Luca Sodomaco, Julian Weigert

We study the locus of square matrices having at least one eigenvector on a prescribed algebraic variety . When is a linear subspace, this data locus is known as the Kalman v…

math.AG2025

Nonlinear Rayleigh quotient optimization

Flavio Salizzoni, Luca Sodomaco, Julian Weigert

Rayleigh quotient minimization deals with optimizing a quadratic homogeneous function over a sphere. Its critical points correspond to the normalized eigenvectors of the symmetric…

math.ST2022★ 1 cited

Log-concave density estimation in undirected graphical models

Kaie Kubjas, Olga Kuznetsova, Elina Robeva +2

We study the problem of maximum likelihood estimation of densities that are log-concave and lie in the graphical model corresponding to a given undirected graph . We show that t…

math.AG2021★ 2 cited

Algebraic degree of optimization over a variety with an application to -norm distance degree

Kaie Kubjas, Olga Kuznetsova, Luca Sodomaco

We study an optimization problem with the feasible set being a real algebraic variety and whose parametric objective function is gradient-solvable with respect to the par…

math.AG2020

Exact solutions in low-rank approximation with zeros

Kaie Kubjas, Luca Sodomaco, Elias Tsigaridas

Low-rank approximation with zeros aims to find a matrix of fixed rank and with a fixed zero pattern that minimizes the Euclidean distance to a given data matrix. We study the criti…