Efficient energy landscape transformation in the problem of binary minimization

A problem of quadratic functional minimization in a discrete space is considered. It is shown that the transformation of a functional by modification of its matrix can significantly accelerate a procedure of a random search. As example we chose two well-known local optimization algorithms: Hopfield neural-network dynamics and Kernighan-Lin algorithm. The proposed method of functional transformation improves efficiency of the both algorithms by many times.