Work

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

Year: 2025

Type: article

Abstract: We study an optimization problem with the feasible set being a real algebraic variety X and whose parametric objective function $$f_u$$ f u is gradient-solvable with respect to the parametric data u. This class of problems includes Euclidean distance and maximum likelihood optimization. For these particular optimization problems, a prominent role is played by the ED and ML correspondence, respectively. We associate an optimization correspondence with our generalized optimization problem and show that it is equidimensional. This leads to the notion of algebraic degree of optimization on X. We apply these results to p-norm optimization and define the p-norm distance degree of X, which coincides with the ED degree of X for $$p=2$$ p = 2 . Finally, we derive a formula for the p-norm distance degree of X as a weighted sum of the polar classes of X under suitable transversality conditions.

Source: Acta Universitatis Sapientiae Mathematica

DOI: 10.1007/s44426-025-00003-1

Authors: Kaie Kubjas, Olga Kuznetsova, Luca Sodomaco

Institutions: Aalto University, Max Planck Institute for Mathematics in the Sciences

Language: English

Topic: Polynomial and algebraic computation

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,390

Funders: Academy of Finland, Max-Planck-Institut für Mathematik in den Naturwissenschaften