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arXiv 2503.18656math.AP

谱Barron空间中Hamilton-Jacobi-Bellman方程的解理论

Solution Theory of Hamilton-Jacobi-Bellman Equations in Spectral Barron Spaces

Ye Feng, Jianfeng Lu

AI总结:

本文研究全空间静态HJB方程在谱Barron空间中的解理论,证明在系数为谱Barron函数且折扣因子足够大时解可被一致有界序列局部一致逼近,从而可用两层神经网络无维数灾难地近似求解。

摘要:

我们研究全空间静态(椭圆)Hamilton-Jacobi-Bellman(HJB)方程在谱Barron空间中的解理论。我们证明,在相关系数为谱Barron函数且折扣因子足够大的假设下,存在一个一致有界的谱Barron函数序列,其局部一致收敛到该解。因此,HJB方程的解可由两层神经网络逼近,且不存在维数灾难。

英文摘要:

We study the solution theory of the whole-space static (elliptic) Hamilton-Jacobi-Bellman (HJB) equation in spectral Barron spaces. We prove that under the assumption that the coefficients involved are spectral Barron functions and the discount factor is sufficiently large, there exists a sequence of uniformly bounded spectral Barron functions that converges locally uniformly to the solution. As a consequence, the solution of the HJB equation can be approximated by two-layer neural networks without curse of dimensionality.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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