Our contribution in this paper is twofold. First, the global convergence analysis of the recently proposed pattern search implicit filtering algorithm (PSIFA), aimed at linearly constrained noisy minimization problems, is revisited to address more general locally Lipschitz objective functions corrupted by noise. Second, PSIFA is applied for solving the damped harmonic oscillator parameter identification problem. This problem can be formulated as a linearly constrained optimization problem, for which the constraints are related to the features of the damping. Such a formulation rests upon a very expensive objective function whose evaluation comprises the numerical solution of an ordinary differential equation (ODE), with intrinsic numerical noise. Computational experimentation encompasses distinct choices for the ODE solvers, and a comparative analysis of the most effective options against the pattern search and the implicit filtering algorithms.

中文翻译：

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应用模式搜索隐式滤波算法解决参数识别中的噪声问题

我们在本文中的贡献是双重的。首先，针对线性约束的噪声最小化问题，对最近提出的模式搜索隐式滤波算法（PSIFA）的全局收敛性分析进行了重新研究，以解决更普遍的局部Lipschitz目标函数受到噪声破坏的问题。其次，将PSIFA用于解决阻尼谐振子参数辨识问题。这个问题可以表述为线性约束优化问题，其约束条件与阻尼特性有关。这种表述基于非常昂贵的目标函数，该函数的评估包括具有固有数值噪声的常微分方程（ODE）的数值解。计算实验包括ODE求解器的不同选择，