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New method tackles high-dimensional Hamilton-Jacobi-Bellman equations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel neural semi-discrete method for solving high-dimensional first-order Hamilton-Jacobi-Bellman (HJB) equations. This approach utilizes centered differences and artificial viscosity to create a monotone operator, which is then evaluated using shifted network queries. The method allows for policy iteration to solve the Bellman equation without the need for a tensor grid, offering improved well-posedness and explicit bounds on the numerical domain of dependence. AI

IMPACT Introduces a new computational method for solving complex HJB equations, potentially impacting fields that rely on such models.

RANK_REASON This is a research paper detailing a new method for solving complex mathematical equations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method tackles high-dimensional Hamilton-Jacobi-Bellman equations

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This is a research paper detailing a new method for solving complex mathematical equations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Minseok Kim, Yeongjong Kim, Namkyeong Cho, Yeoneung Kim ·

    Monotone Neural Policy Iteration for High-Dimensional First-Order Hamilton--Jacobi--Bellman Equations

    arXiv:2605.07116v2 Announce Type: replace Abstract: We analyze a neural semi-discrete method for high-dimensional first-order Hamilton-Jacobi-Bellman (HJB) equations with known or learned dynamics. Centered differences and an artificial viscosity $Nh=O(h)$ define a monotone opera…

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