In this paper, we introduce the p-Fourier Discrepancy Functions, a new family of metrics for comparing discrete probability measures, inspired by the χr-metrics. Unlike the χr-metrics, the p-Fourier Discrepancies are well-defined for any pair of measures. We prove that the p-Fourier Discrepancies are convex, twice differentiable, and that their gradient has an explicit formula. Moreover, we study the lower and upper tight bounds for the p-Fourier Discrepancies in terms of the Total Variation distance.

The Fourier Discrepancy Function

Auricchio, Gennaro
Membro del Collaboration Group
;
Codegoni, Andrea
Membro del Collaboration Group
;
Gualandi, Stefano
Membro del Collaboration Group
;
Zambon, Lorenzo
Membro del Collaboration Group
2023-01-01

Abstract

In this paper, we introduce the p-Fourier Discrepancy Functions, a new family of metrics for comparing discrete probability measures, inspired by the χr-metrics. Unlike the χr-metrics, the p-Fourier Discrepancies are well-defined for any pair of measures. We prove that the p-Fourier Discrepancies are convex, twice differentiable, and that their gradient has an explicit formula. Moreover, we study the lower and upper tight bounds for the p-Fourier Discrepancies in terms of the Total Variation distance.
2023
Engineering Mathematics covers resources on applied mathematics, mathematical modelling, combinatorics, optimization techniques, numerical methods, and statistical methods that have an emphasis on engineering systems.
Esperti anonimi
Inglese
Internazionale
STAMPA
21
3
627
639
13
https://arxiv.org/abs/2102.02979
4
info:eu-repo/semantics/article
262
Auricchio, Gennaro; Codegoni, Andrea; Gualandi, Stefano; Zambon, Lorenzo
1 Contributo su Rivista::1.1 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1474361
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