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

Abstract We study an optimization problem with the feasible set being a real algebraic variety X and whose parametric objective function $$f_u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>u</mml:mi> </mml:msub> </mml:math> 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 $$p=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> . 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.

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Journal
Acta Universitatis Sapientiae Mathematica
Published
2025-05-27
DOI
https://doi.org/10.1007/s44426-025-00003-1
Citations
1
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

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

O. I. Kuznetsova, Kaie Kubjas, Luca Sodomaco
1 citations
Acta Universitatis Sapientiae Mathematica
Advanced Numerical Analysis Techniques
article

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

O. I. Kuznetsova, Kaie Kubjas, Luca Sodomaco
article en
1 citations

Abstract

Abstract We study an optimization problem with the feasible set being a real algebraic variety X and whose parametric objective function $$f_u$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>f</mml:mi> <mml:mi>u</mml:mi> </mml:msub> </mml:math> 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 $$p=2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>p</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> . 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.

Acta Universitatis Sapientiae MathematicaVol. 17(1)
Max Planck Institute for Mathematics in the Sciences (DE), Max Planck Institute for Mathematics (DE), Aalto University (FI)
Academy of Finland, Max-Planck-Institut für Mathematik in den Naturwissenschaften
Openalex Percentile: Top 100%
Advanced Numerical Analysis Techniques
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Algebraic degree of optimization over a variety with an application to P-norm distance degree — O. I. Kuznetsova, Kaie Kubjas, et al. · Acta Universitatis Sapientiae Mathematica (2025) | TGRS Research Map | TGRS