Identifiability, and the closely related idea of partial smoothness, unify classical active set methods and more general notions of solution structure. Diverse optimization algorithms generate iterates in discrete time that are eventually confined to identifiable sets. We present two fresh perspectives on identifiability. The first distills the notion to a simple metric property, applicable not just in Euclidean settings but to optimization over manifolds and beyond; the second reveals analogous continuous-time behavior for subgradient descent curves. The Kurdyka–Łojasiewicz property typically governs convergence in both discrete and continuous time: we explore its interplay with identifiability.

可识别性以及密切相关的部分平滑概念统一了经典的活动集方法和更一般的解结构概念。不同的优化算法在离散时间内生成迭代，最终限制在可识别的集合中。我们提出了关于可识别性的两种新观点。第一个将概念提炼为简单的度量属性，不仅适用于欧几里得设置，而且适用于流形及其他方面的优化；第二个揭示了次梯度下降曲线的类似连续时间行为。 Kurdyka-Łojasiewicz 属性通常控制着离散时间和连续时间的收敛：我们探索它与可识别性的相互作用。