Regularized unbalanced optimal transport as entropy minimization with respect to branching Brownian motion/ A. Baradat and H. Lavenant

By:

Baradat, A [author]

Contributor(s):

Lavenant, H [author]

Material type: Text

TextSeries: Astérisque ; 458Publication details: Paris: Société Mathématique de France, 2025Description: viii, 194 pages: ill.; 25 cmISBN:

9782379052118

Subject(s):

DDC classification:

23rd 519.233 B223

Summary: The monograph connects a branching Schrödinger problem (minimizing relative entropy with respect to a branching Brownian motion) with regularized unbalanced optimal transport. Using duality and probabilistic analysis, the authors show equivalences between the two formulations, characterize laws with finite entropy relative to branching Brownian motion, analyze the small-noise (diffusivity → 0) limit (linking to partial optimal transport), and present numerical discretizations solved via convex optimization.

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Books	ISI Library, Kolkata	519.233 B223 (Browse shelf(Opens below))	Available		C27813

Total holds: 0

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Previous	519.233 As844 Continuous strong Markov processes in dimension one	519.233 B168 Analysis and geometry of Markov diffusion operators /	519.233 B176 Functionals of multidimensional diffusions with applications to finance /	519.233 B223 Regularized unbalanced optimal transport as entropy minimization with respect to branching Brownian motion/	519.233 B241 Semi-Markov chains and hidden semi-Markov models toward applications	519.233 B292 Latent Markov models for longitudinal data	519.233 B575 Elements of the theory of Markov processes and their applications	Next

Includes bibliography

The monograph connects a branching Schrödinger problem (minimizing relative entropy with respect to a branching Brownian motion) with regularized unbalanced optimal transport. Using duality and probabilistic analysis, the authors show equivalences between the two formulations, characterize laws with finite entropy relative to branching Brownian motion, analyze the small-noise (diffusivity → 0) limit (linking to partial optimal transport), and present numerical discretizations solved via convex optimization.

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