Sharp inequalities between Zolotarev and Wasserstein distances in $$\mathcal {P}_2(\mathbb {R}^d)$$

Abstract Based on a new Kantorovich–Rubinstein duality principle for the Hessian that was recently established by the two authors, we extend the Rio inequality to any dimension $$d \ge 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> with an optimal constant. Similarly, we propose an optimal upper bound for the ratio of Zolotarev distance $$Z_2(\mu ,\nu )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Z</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> to Wasserstein distance $$W_2(\mu ,\nu )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>W</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> when $$\mu ,\nu \in \mathcal {P}_2(\mathbb {R}^d)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>∈</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> are centred probabilities with prescribed variances.

Authors

Institutions

Publication Details

Journal
Probability Theory and Related Fields
Published
2026-07-30
DOI
https://doi.org/10.1007/s00440-026-01527-4
Primary Topic
Geometric Analysis and Curvature Flows
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sharp inequalities between Zolotarev and Wasserstein distances in $$\mathcal {P}_2(\mathbb {R}^d)$$

Karol Bołbotowski, Guy Bouchitté
Probability Theory and Related Fields
Geometric Analysis and Curvature Flows
article

Sharp inequalities between Zolotarev and Wasserstein distances in $$\mathcal {P}_2(\mathbb {R}^d)$$

Karol Bołbotowski, Guy Bouchitté
article en

Abstract

Abstract Based on a new Kantorovich–Rubinstein duality principle for the Hessian that was recently established by the two authors, we extend the Rio inequality to any dimension $$d \ge 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> with an optimal constant. Similarly, we propose an optimal upper bound for the ratio of Zolotarev distance $$Z_2(\mu ,\nu )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>Z</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> to Wasserstein distance $$W_2(\mu ,\nu )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>W</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> when $$\mu ,\nu \in \mathcal {P}_2(\mathbb {R}^d)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>μ</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ν</mml:mi> <mml:mo>∈</mml:mo> <mml:msub> <mml:mi>P</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> are centred probabilities with prescribed variances.

Probability Theory and Related Fields
Warsaw University of Technology (PL), Université de Toulon (FR), Medical University of Warsaw (PL), University of Warsaw (PL)
Agence Nationale de la Recherche
Openalex Percentile: Top 5%
Geometric Analysis and Curvature Flows
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.