Charnes, A., Cooper, W. W., & Rhodes, E. (1979). Measuring the efficiency of decision-making units. European journal of operational research, 3(4), 339.
Hu, X. (2009). Applications of the general projection neural network in solving extended linear-quadratic programming problems with linear constraints. Neurocomputing, 72 (4), 1131-1137.
Hu, X., Zhang, B. (2009). A New Recurrent Neural Network for Solving Convex Quadratic Programming Problems with an Application to the-Winners-Take-All Problem. Neural Networks, IEEE Transactions on, 20(4), 654-664.
Kennedy, M. P., Chua, L. O. (1988). Neural networks for nonlinear programming. Circuits and Systems, IEEE Transactions on, 35(5), 554-562.
Kinderlehrer, D., & Stampacchia, G. (1980). An introduction to variational inequalities and their applications (Vol. 31). Siam.
Liu, Q., Wang, J. (2013). A one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints. Neural Networks and Learning Systems, IEEE Transactions on, 24(5), 812-824.
Maa, C. Y., Shanblatt, M. A. (1992). Linear and quadratic programming neural network analysis. Neural Networks, IEEE Transactions on, 3(4), 580-594.
Miller, R. K., & Michel, A. N. (1982). Ordinary differential equations. Academic Press.
Nazemi, A. (2014). A neural network model for solving convex quadratic programming problems with some applications. Engineering Applications of Artificial Intelligence, 32, 54-62.
Nazemi, A., Nazemi, M. (2014). A Gradient-Based Neural Network Method for Solving Strictly Convex Quadratic Programming Problems. Cognitive Computation. 1-12.
Ortega, J. M., & Rheinboldt, W. C. (1970). Iterative solution of nonlinear equations in several variables (Vol. 30). Siam.
Rodriguez-Vazquez, A., Dominguez-Castro, R., Rueda, A., Huertas, J. L., Sanchez-Sinencio, E. (1990). Nonlinear switched capacitor neural networks for optimization problems.Circuits and Systems, IEEE Transactions on, 37(3), 384-398.
Tank, D., Hopfield, J. J. (1986). Simple ’neural’ optimization networks: An A/D converter, signal decision circuit, and a linear programming circuit. Circuits and Systems, IEEE Transactions on, 33(5), 533-541.
Wu, X. Y., Xia, Y. S., Li, J., Chen, W. K. (1996). A high-performance neural network for solving linear and quadratic programming problems. Neural Networks, IEEE Transactions on, 7(3), 643-651.
Xia, Y. (1996). A new neural network for solving linear and quadratic programming problems. Neural Networks, IEEE Transactions on, 7(6), 1544-1548.
Xia, Y. (2009). A Compact Cooperative Recurrent Neural Network for Computing General Constrained Norm Estimators. Signal Processing, IEEE Transactions on, 57(9),3693-3697.
Xia, Y. S., Wang, J. (2000a). On the stability of globally projected dynamical systems. Journal of Optimization Theory and Applications, 106(1), 129-150.
Xia, Y., & Wang, J. (2016). A bi-projection neural network for solving constrained quadratic optimization problems. IEEE transactions on neural networks and learning systems, 27(2), 214-224.
Xia, Y., Feng, G., Wang, J. (2004). A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations. Neural Networks, 17(7), 1003-1015.
Xia, Y., Feng, G., Wang, J. (2008). A novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. Neural Networks, IEEE Transactions on, 19(8), 1340-1353.
Xia, Y., Leung, H. (2014). A Fast Learning Algorithm for Blind Data Fusion Using a Novel-Norm Estimation. Sensors Journal, IEEE, 14(3), 666-672.
Xia, Y., Leung, H., Wang, J. (2002). A projection neural network and its application to constrained optimization problems. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on, 49(4), 447-458.
Xia, Y., Sun, C., Zheng, W. X. (2012). Discrete-time neural network for fast solving large linear estimation problems and its application to image restoration. Neural Networks and Learning Systems, IEEE Transactions on, 23(5), 812-820.
Xia, Y., Wang, J. (1998). A general methodology for designing globally convergent optimization neural networks. Neural Networks, IEEE Transactions on, 9(6), 1331-1343.
Xia, Y., Wang, J. (2000b). A recurrent neural network for solving linear projection equations. Neural Networks, 13(3), 337-350.
Xia, Y., Wang, J. (2000c). Global exponential stability of recurrent neural networks for solving optimization and related problems. Neural Networks, IEEE Transactions on,11(4), 1017-1022.
Xia, Y., Wang, J. (2004). A general projection neural network for solving monotone variational inequalities and related optimization problems. Neural Networks, IEEE Transactions on, 15(2), 318-328.
Xia, Y., Wang, J. (2005). A recurrent neural network for solving nonlinear convex programs subject to linear constraints. Neural Networks, IEEE Transactions on, 16(2),379-386.
Xue X, Bian W. (2007). A project neural network for solving degenerate convex quadratic program. Neurocomputing.70:244959. 46.
Yan, Y. (2014). A New Nonlinear Neural Network for Solving QP Problems. In Advances in Neural Networks ISNN 2014 (pp. 347-357). Springer International Publishing.
Yang Y, Cao J. (2008). A feedback neural network for solving convex constraint optimization problems. Appl Math Comput. 201: 34050. 47.
Yang, Y., Cao, J., Xu, X., Hu, M., & Gao, Y. (2014). A new neural network for solving quadratic programming problems with equality and inequality constraints. Mathematics and Computers in Simulation, 101, 103-112.
Zhang, J., & Zhang, L. (2010). An augmented Lagrangian method for a class of inverse quadratic programming problems. Applied Mathematics and Optimization, 61(1), 57.