  This paper tackles the challenging problem of output range estimation for
Deep Neural Networks (DNNs), introducing a novel algorithm based on Simulated
Annealing (SA). Our approach addresses the lack of local geometric information
and high non-linearity in DNNs, making it versatile across various
architectures, especially Residual Neural Networks (ResNets). We present a
straightforward, implementation-friendly algorithm that avoids restrictive
assumptions about network architecture. Through theoretical analysis and
experimental evaluations, including tests on the Ackley function, we
demonstrate our algorithm's effectiveness in navigating complex, non-convex
surfaces and accurately estimating DNN output ranges. Futhermore, the Python
codes of this experimental evaluation that support our results are available in
our GitHub repository
(https://github.com/Nicerova7/output-range-analysis-for-deep-neural-networks-with-simulated-annealing).
