Simulation of an Analogue Circuit Solving NP-Hard Optimization Problems

Optimization methods took a leap forward in the past years and with the appearance of deep neural networks and gradient based methods, various complex and differentiable problems become solvable in myriads of practical tasks. Meanwhile optimization of discrete problems is still a challenging task, where efficient solvers are required. In this paper we present a novel analogue circuit in simulation, inspired by the dynamics of cellular neural networks, which can solve NP-hard optimization problems in continuous time. The dynamics were tested in MATLAB and PSPICE and it is shown via moderately complex problems that the dynamics of the circuit converges to the optimal solution in milliseconds with low power consumption.