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Researchers develop Sinkhorn with memory for control-affine Schrödinger bridge problem

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a novel Sinkhorn recursion with memory to address the control-affine Schrödinger bridge problem when input and noise channels do not match. This new algorithm overcomes the limitations of existing methods, which are only applicable when these channels are proportional. The paper demonstrates the algorithm's ability to solve these complex nonlinear PDEs and proves its local stability. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a new computational method for optimal control policies in diffusion processes, potentially impacting AI research in related fields.

RANK_REASON Academic paper detailing a new algorithmic approach to a mathematical problem.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Georgiy A. Bondar, Asmaa Eldesoukey, Yongxin Chen, Abhishek Halder ·

    Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schr\"{o}dinger Bridge Problem

    arXiv:2604.23370v1 Announce Type: cross Abstract: Solutions to the Schr\"{o}dinger bridge problem and its generalizations yield feedback control policies for optimal density steering over a controlled diffusion. To numerically compute the same, the dynamic Sinkhorn recursion has …

  2. arXiv stat.ML TIER_1 · Abhishek Halder ·

    Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schrödinger Bridge Problem

    Solutions to the Schrödinger bridge problem and its generalizations yield feedback control policies for optimal density steering over a controlled diffusion. To numerically compute the same, the dynamic Sinkhorn recursion has become a standard approach. The mathematical engine be…

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