Kobra Mohammadsalahi; Farzin Modarres Khiyabani; Nima Azarmir Shotorbani
Abstract
This paper presents a capable recurrent neural network, the so-called µRNN for solving a class of non-convex quadratic programming problems. Based on the optimality conditions we construct a new recurrent neural network (µRNN), which has a simple structure and its capability ...
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This paper presents a capable recurrent neural network, the so-called µRNN for solving a class of non-convex quadratic programming problems. Based on the optimality conditions we construct a new recurrent neural network (µRNN), which has a simple structure and its capability is preserved. The proposed neural network model is stable in the sense of Lyapunov and converges to the exact optimal solution of the original problem. In a particular case, the optimality conditions of the problem become necessary and sufficient. Numerical experiments and comparisons with some existing algorithms are presented to illustrate the theoretical results and show the efficiency of the proposed network.
Control and Optimization
Alaeddin Malek; Ghasem Ahmadi; Seyyed Mehdi Mirhoseini Alizamini
Volume 1, Issue 1 , April 2016, , Pages 55-67
Abstract
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization ...
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Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use a recurrent neural network model, with a simple structure based on a dynamical system to solve this problem. The portfolio selection problem and some other numerical examples are solved to evaluate the effectiveness of the presented model.
