A polarization tensor approximation for the Hessian in iterative solvers for non-linear inverse problems

A polarization tensor approximation for the Hessian in iterative solvers for non-linear inverse problems
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非线性反问题迭代求解器中 Hessian 矩阵的偏振张量近似

DOI:
10.1080/17415977.2021.1951722
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发表时间:
2021
影响因子:
1.3
通讯作者:
Watson F
Watson F
中科院分区:
工程技术4区
文献类型:
--
作者:
Watson F

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对于许多偏微分方程的反参数问题,其中的域只包含分离良好的对象,一个渐进的解决方案,涉及“极化张量”的前向问题存在。这些是夹杂物的尺寸和材料对比度的函数,从而描述了非线性的饱和分量。在本文中,我们展示了如何这样的渐近级数可以应用于非线性最小二乘重建问题,通过推导出一个近似的对角Hessian矩阵的数据失配项。通常,Hessian矩阵可以在处理非线性方面发挥至关重要的作用,生成良好的更新方向,从而加速解决方案朝向全局最小值,但计算成本可能使直接计算不可行。由于极化张量近似假设夹杂物之间有足够的分离,我们的近似海森不考虑非线性的形式缺乏叠加的反问题。然而,它确实说明了随着材料对比度的增加,数据变化的非线性饱和。因此,我们建议使用它作为一个初始的Hessian拟牛顿计划。我们目前的数值实验的近似Hessian的电阻抗断层成像的情况下的准确性和重建性能,提供了一个证明的重建方案的原则。
For many inverse parameter problems for partial differential equations in which the domain contains only well-separated objects, an asymptotic solution to the forward problem involving ‘polarization tensors’ exists. These are functions of the size and material contrast of inclusions, thereby describing the saturation component of the non-linearity. In this paper, we show how such an asymptotic series can be applied to non-linear least-squares reconstruction problems, by deriving an approximate diagonal Hessian matrix for the data misfit term. Often, the Hessian matrix can play a vital role in dealing with the non-linearity, generating good update directions which accelerate the solution towards a global minimum, but the computational cost can make direct calculation infeasible. Since the polarization tensor approximation assumes sufficient separation between inclusions, our approximate Hessian does not account for non-linearity in the form of lack of superposition in the inverse problem. It does, however, account for the non-linear saturation of the change in the data with increasing material contrast. We, therefore, propose to use it as an initial Hessian for quasi-Newton schemes. We present numerical experimentation into the accuracy and reconstruction performance of the approximate Hessian for the case of electrical impedance tomography, providing a proof of principle of the reconstruction scheme.
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