Computable cone bounds provide rigorous spectral enclosure with differentiable soft-min/max surrogates that maintain explicit approximation errors

computable lower and upper cone bounds provide an a posteriori enclosure of a distinguished cone level, while smooth soft-min/max surrogates preserve rigorous one-sided bounds with explicit approximation errors and remain differentiable with respect to the trainable parameters.

http://arxiv.org/abs/2608.27122v1