Finite-Time Horizon, Stopper vs. Singular-Controller Games on the Half-Line

We prove the existence of a value for two-player zero-sum stopper versus singular-controller games on a finite-time horizon when the underlying dynamics are one-dimensional, diffusive and bound to evolve in [Formula: see text]. We show that the value is the maximal solution of a variational inequality with both obstacle and gradient constraint and satisfying a Dirichlet boundary condition at [Formula: see text]. Moreover, we obtain an optimal strategy for the stopper. In order to achieve our goals, we rely on new probabilistic methods, yielding gradient bounds and equicontinuity for the solutions of penalised partial differential equations that approximate the variational inequality. Funding: Both authors received partial financial support from the European Union (NextGenerationEU) PRIN2022 [Grant 2022BEMMLZ; CUP: D53D23005780006]. T. De Angelis also received partial financial support from PRIN-PNRR2022 [Grant P20224TM7Z; CUP: D53D23018780001].

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Publication Details

Journal
Mathematics of Operations Research
Published
2026-01-02
DOI
https://doi.org/10.1287/moor.2024.0690
Primary Topic
Optimization and Variational Analysis
Type
article
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article

Finite-Time Horizon, Stopper vs. Singular-Controller Games on the Half-Line

Tiziano De Angelis, Andrea Bovo
Mathematics of Operations Research
Optimization and Variational Analysis
article

Finite-Time Horizon, Stopper vs. Singular-Controller Games on the Half-Line

Tiziano De Angelis, Andrea Bovo
article en

Abstract

We prove the existence of a value for two-player zero-sum stopper versus singular-controller games on a finite-time horizon when the underlying dynamics are one-dimensional, diffusive and bound to evolve in [Formula: see text]. We show that the value is the maximal solution of a variational inequality with both obstacle and gradient constraint and satisfying a Dirichlet boundary condition at [Formula: see text]. Moreover, we obtain an optimal strategy for the stopper. In order to achieve our goals, we rely on new probabilistic methods, yielding gradient bounds and equicontinuity for the solutions of penalised partial differential equations that approximate the variational inequality. Funding: Both authors received partial financial support from the European Union (NextGenerationEU) PRIN2022 [Grant 2022BEMMLZ; CUP: D53D23005780006]. T. De Angelis also received partial financial support from PRIN-PNRR2022 [Grant P20224TM7Z; CUP: D53D23018780001].

Mathematics of Operations Research
Collegio Carlo Alberto (IT), University of Brescia (IT)
Reduced inequalities
Openalex Percentile: Top 97%
Optimization and Variational Analysis
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