all AI news
[D] Non-Differentiable Loss Functions
Do Problems in Combinatorial Discrete Optimization (generally) Have Non-Differentiable Loss Functions?
I was trying to better understand this: we can only take the derivatives of functions if they have certain properties (https://en.wikipedia.org/wiki/Differentiable_function : e.g. continuous, no indicator variables within them, etc.).
Considering the nature of many typical problems in combinatorial discrete optimization (e.g. scheduling flights, shortest path, travelling salesmen problem, knapsack problem) - at first thought, it does not seem like the objective functions of these problems are "differentiable" …!-->