求解逆Sturm-Liouville问题的最小二乘泛函
A Least Squares Functional for Solving Inverse Sturm-Liouville Problems
AI总结:
提出一种变分算法,通过最小二乘泛函的全局最小值性质,利用共轭梯度下降法从两个谱数据反演一维Sturm-Liouville问题,数值实验表明算法可靠且快速收敛。
摘要:
我们提出一种变分算法,用于在给定两个谱的情况下求解经典的一维逆Sturm-Liouville问题。最小二乘泛函的所有临界点均为全局最小值,这提示我们可以通过(共轭)梯度下降算法进行最小化。数值例子表明,即使存在噪声,所得到的算法在无需针对特定情况调整的情况下也能可靠地工作。通过正确的参数选择,最小化过程收敛非常快。
英文摘要:
We present a variational algorithm for solving the classical inverse Sturm-Liouville problem in one dimension when two spectra are given. All critical points of the least squares functional are at global minima, which which suggests minimization by a (conjugate) gradient descent algorithm. Numerical examples show that the resulting algorithm works quite reliable without tuning for particular cases, even in the presence of noise. With the right choice of parameters the minimization procedure converges very quickly.
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