Non-convex continuous optimization problems occur in many fields of engineering, ranging from operations research, control theory and neural network learning. The most common optimization approach are gradient based method that,however, for large optimization problems, these approaches are plagued by local minima and saddle points,that is suboptimal solutions where the gradients are zero and where optimization halts prematurely.Recently, Baldassi et al. [ https://doi.org/10.1073/pnas.1608103113] have introduced a local entropic measure for learning with discrete synapses that leads to unanticipated computational performance.Inspired by this line of work, Chaudhari [ arXiv:1611.01838] connects this idea to the solution of a Hamilton-Jacobi partial differential equation and stochastic optimal control theory, providing an algorithm to implement the descent along the gradient of the local entropy.