Hitting time mixing for the random transposition walk

Abstract Consider shuffling a deck of n cards, labeled 1 through n , as follows: at each time step, pick one card uniformly with your right hand and another card, independently and uniformly with your left hand; then swap the cards. How long does it take until the deck is close to random? Diaconis and Shahshahani showed that this process undergoes cutoff in total variation distance at time $$t = \lfloor (n\log {n})/2 \rfloor $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mo>⌊</mml:mo> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> <mml:mo>⌋</mml:mo> </mml:mrow> </mml:math> . Confirming a conjecture of N. Berestycki, we prove the definitive “hitting time” version of this result: let $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> denote the first time at which all cards have been touched. The total variation distance between the stopped distribution at $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> and the uniform distribution on permutations is o (1); this is best possible, since at time $$\tau -1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>τ</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , the total variation distance is at least $$(1+o(1))e^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:msup> <mml:mi>e</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> .

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Journal
Probability Theory and Related Fields
Published
2026-03-16
DOI
https://doi.org/10.1007/s00440-026-01479-9
Primary Topic
Markov Chains and Monte Carlo Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Hitting time mixing for the random transposition walk

Vishesh Jain, Mehtaab Sawhney
Probability Theory and Related Fields
Markov Chains and Monte Carlo Methods
article

Hitting time mixing for the random transposition walk

Vishesh Jain, Mehtaab Sawhney
article en

Abstract

Abstract Consider shuffling a deck of n cards, labeled 1 through n , as follows: at each time step, pick one card uniformly with your right hand and another card, independently and uniformly with your left hand; then swap the cards. How long does it take until the deck is close to random? Diaconis and Shahshahani showed that this process undergoes cutoff in total variation distance at time $$t = \lfloor (n\log {n})/2 \rfloor $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mo>⌊</mml:mo> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>log</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> <mml:mo>⌋</mml:mo> </mml:mrow> </mml:math> . Confirming a conjecture of N. Berestycki, we prove the definitive “hitting time” version of this result: let $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> denote the first time at which all cards have been touched. The total variation distance between the stopped distribution at $$\tau $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>τ</mml:mi> </mml:math> and the uniform distribution on permutations is o (1); this is best possible, since at time $$\tau -1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>τ</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> , the total variation distance is at least $$(1+o(1))e^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>+</mml:mo> <mml:mi>o</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> <mml:msup> <mml:mi>e</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> .

Probability Theory and Related Fields
University of Illinois Chicago (US), University of Chicago (US), Columbia University (US)
National Science Foundation
Openalex Percentile: Top 25%
Markov Chains and Monte Carlo Methods
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Hitting time mixing for the random transposition walk — Vishesh Jain, Mehtaab Sawhney · Probability Theory and Related Fields (2026) | TGRS Research Map | TGRS