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arXiv 2007.15052math.AP
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正定张量场导数乘积的锐非线性估计

Sharp nonlinear estimates for multiplying derivatives of positive definite tensor fields

Michal Bathory

AI总结:

本文将标量函数幂次导数乘积公式推广到对称正定矩阵值函数,利用积分表示与对数凸性克服非交换性,建立了锐非线性估计,并用实例验证了结果的最优性。

摘要:

本文将标量函数不同幂次导数的简单乘积公式推广到取值于对称正定矩阵集合的函数。这些公式是推导各种非线性估计的基础,尤其是在偏微分方程理论中。为了克服矩阵与其导数的非交换性,我们应用了一些众所周知的积分表示公式,然后观察到矩阵幂的导数关于指数是对数凸函数。这直接关系到一个看似简单的不等式的有效性,该不等式结合了积分平均与矩阵上的内积。我们结果的最优性通过大量例子加以说明。

英文摘要:

The simple product formulae for derivatives of scalar functions raised to different powers are generalized for functions which take values in the set of symmetric positive definite matrices. These formulae are fundamental in derivation of various non-linear estimates, especially in the PDE theory. To get around the non-commutativity of the matrix and its derivative, we apply some well-known integral representation formulas and then we make an observation that the derivative of a matrix power is a logarithmically convex function with respect to the exponent. This is directly related to the validity of a seemingly simple inequality combining the integral averages and the inner product on matrices. The optimality of our results is illustrated on numerous examples.

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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