“…For example, a deterministic annealing neural network was proposed for solving convex programming problems (Wang, 1994), a Lagrangian network was developed for solving convex optimization problems with linear equality constraints based on the Lagrangian optimality conditions (Xia, 2003), the primal-dual network (Xia, 1996), the dual network (Xia, Feng, & Wang, 2004), and the simplified dual network (Liu & Wang, 2006) were developed for solving convex optimization problems based on the Karush-Kuhn-Tucker optimality conditions, projection neural networks were developed for constrained optimization problems based on the projection method (Gao, 2004;Hu & Wang, 2007;Liu, Cao, & Chen, 2010;Xia, Leung, & Wang, 2002). In recent years, neurodynamic optimization approaches have been extended to nonconvex and generalized convex optimization problems.…”