A practical challenge for structural estimation is the requirement to accurately minimize a sample objective function which is often non-smooth, non-convex, or both. This paper proposes a simple algorithm designed to find accurate solutions without performing an exhaustive search. It augments each iteration from a new Gauss-Newton algorithm with a grid search step. A finite sample analysis derives its optimization and statistical properties simultaneously using only econometric assumptions. After a finite number of iterations, the algorithm automatically transitions from global to fast local convergence, producing accurate estimates with high probability. Simulated examples and an empirical application illustrate the results.

翻译：结构估计的一个实际挑战是需要精确最小化一个样本目标函数，该函数通常非光滑、非凸或兼具两者。本文提出一种简单算法，旨在无需穷举搜索即可找到精确解。该算法在新高斯-牛顿算法的每次迭代中附加网格搜索步骤。有限样本分析仅使用计量经济学假设，同时推导出其优化与统计性质。经过有限次迭代后，算法自动从全局收敛过渡到快速局部收敛，以高概率产生精确估计值。模拟示例和实证应用验证了上述结果。