Extending Wormald’s differential equation method to one-sided bounds

Abstract In this note, we formulate a ‘one-sided’ version of Wormald’s differential equation method. In the standard ‘two-sided’ method, one is given a family of random variables that evolve over time and which satisfy some conditions, including a tight estimate of the expected change in each variable over one-time step. These estimates for the expected one-step changes suggest that the variables ought to be close to the solution of a certain system of differential equations, and the standard method concludes that this is indeed the case. We give a result for the case where instead of a tight estimate for each variable’s expected one-step change, we have only an upper bound. Our proof is very simple and is flexible enough that if we instead assume tight estimates on the variables, then we recover the conclusion of the standard differential equation method.

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Publication Details

Journal
Combinatorics Probability Computing
Published
2025-02-06
DOI
https://doi.org/10.1017/s0963548325000033
Primary Topic
Matrix Theory and Algorithms
Type
article
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article

Extending Wormald’s differential equation method to one-sided bounds

Patrick Bennett, Calum MacRury
Combinatorics Probability Computing
Matrix Theory and Algorithms
article

Extending Wormald’s differential equation method to one-sided bounds

Patrick Bennett, Calum MacRury
article en

Abstract

Abstract In this note, we formulate a ‘one-sided’ version of Wormald’s differential equation method. In the standard ‘two-sided’ method, one is given a family of random variables that evolve over time and which satisfy some conditions, including a tight estimate of the expected change in each variable over one-time step. These estimates for the expected one-step changes suggest that the variables ought to be close to the solution of a certain system of differential equations, and the standard method concludes that this is indeed the case. We give a result for the case where instead of a tight estimate for each variable’s expected one-step change, we have only an upper bound. Our proof is very simple and is flexible enough that if we instead assume tight estimates on the variables, then we recover the conclusion of the standard differential equation method.

Combinatorics Probability Computing
Western Michigan University (US), Columbia University (US)
Openalex Percentile: Top 97%
Matrix Theory and Algorithms
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