Study of neural network models for linear programming

The article explores a method for determining the motion vector from hyperplanes that bound the feasible polytop of a multidimensional linear programming problem. The method is based on visual images fed to the input of a feedforward neural network. The visualization algorithm constructs a receptive field in the vicinity of a point located on the bounding hyperplane. For each point of the receptive field, the scalar bias to the hyperplane surface is calculated. Based on the calculated bias, each receptive field point is assigned with a scalar value. The resulting visual image is fed to the input of a feed-forward neural network, which calculates the direction of maximum increase in the objective function on the bounding hyperplane. The article proposes an improved form of the cross-shaped receptive field. The construction of a training set based on randomly generated bounding hyperplanes and objective functions in multidimensional spaces is described. A scalable neural network architecture with a variable number of hidden layers has been developed. The hyperparameters of the neural network were selected. Computational experiments confirmed the high (more than 98%) accuracy of the cross-shaped receptive field. The dependence of the accuracy of the neural network results on the number of hidden layers and the duration of training was studied.