Constrained optimization of the parameters of a simulator plays a crucial role in a design process. These problems become challenging when the simulator is stochastic, computationally expensive, and the parameter space is high-dimensional. One can efficiently perform optimization only by utilizing the gradient with respect to the parameters, but these gradients are unavailable in many legacy, black-box codes. We introduce the algorithm Scout-Nd (Stochastic Constrained Optimization for N dimensions) to tackle the issues mentioned earlier by efficiently estimating the gradient, reducing the noise of the gradient estimator, and applying multi-fidelity schemes to further reduce computational effort. We validate our approach on standard benchmarks, demonstrating its effectiveness in optimizing parameters highlighting better performance compared to existing methods.

翻译：模拟器参数的约束优化在设计过程中至关重要。当模拟器具有随机性、计算成本高昂且参数空间为高维时，这些问题变得颇具挑战性。尽管通过利用参数梯度可以高效执行优化，但在许多传统黑箱代码中，这些梯度并不可用。我们提出Scout-Nd算法（N维随机约束优化）以应对上述挑战，该算法通过高效估计梯度、降低梯度估计器的噪声，并应用多保真度方案进一步减少计算开销。我们在标准基准测试中验证了该方法，证明了其在参数优化方面的有效性，与现有方法相比展现出更优性能。