A novel global optimization algorithm using DC envelope and convex cut function

This work presents a novel global optimization algorithm which is applicable to general NLP's consist of twice differentiable objective and constraint functions. The algorithm iteratively generates continuous piecewise concave underestimators, difference of convex (d.c.) envelope, of objective function and it generates convex cut functions of constraint functions when acquired lower bound is located at infeasible region.