LMLipschitz
LMLipschitz is a term used in mathematics and machine learning to describe a Lipschitz-like condition in which the Lipschitz bound may vary with the input. It is often discussed in the context of neural networks and function approximation, where local control of a function’s growth is desirable for stability and robustness.
Formally, a function f: X → Y is described as LMLipschitz if for every x in X there
In practice, LMLipschitz is used to describe learned or estimated local Lipschitz bounds. Methods to compute
Applications of LMLipschitz concepts appear in certified robustness, safe reinforcement learning, and generalization analyses where local
See also: Lipschitz continuity, spectral normalization, Jacobian regularization, adversarial robustness.