A Smoothing Technique for Objective Penalty Functions in Inequality-Constrained Optimization: Applications in Wireless Sensor Networks and 5G Communication

This manuscript provides a smoothing technique for objective penalty functions in inequality-constrained optimization problems. A non-smooth penalty function is defined which is subjected to a new smoothing technique to make it smooth. The error estimates for the original and the smoothed problem are discussed. A procedure is illustrated for the development of the solution of the inequality-constrained optimization problem and is shown to be convergent under certain specified conditions. The same can be incorporated in various application areas like Wireless Sensor Networks in the form of giving penalties to sensor nodes not fulfilling the network performance criteria and also in some other aspects like 5G communication.