Linear programmingPolyhedral theoryPOLYNOMIAL-TIME ALGORITHMDeep learning has received much attention lately due to the impressive empirical performance achieved by training algorithms. Consequently, a need for a better theoretical understanding of these problems has become more evident and multiple works in ...
We propose and analyse a new class of neural network models for solving linear programming (LP) problems in real time. We introduce a novel energy function that transforms linear programming into a system of nonlinear differential equations. This system of differential equations can be solved on-li...
We demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applie...
Solution of linear programming prob- lems using a neural network with non-linear feedback. Radioengineering, 21(4):1171, 2012.RAHMAN, S. A., ANSARI, M. S., MOINUDDIN, A. A. Solution of linear programming problems using a neural network with non- linear feedback. Radioengineering, 2012,...
As a powerful modelling method, piecewise linear neural networks (PWLNNs) have proven successful in various fields, most recently in deep learning. To apply PWLNN methods, both the representation and the learning have long been studied. In 1977, the cano
Coordinate descent algorithm for ramp loss linear programming support vector machines. Neural Process. Lett. 43, 887–903 (2016). Article Google Scholar Xu, Z., Liu, K., Xi, X. & Wang, S. in Proc. IEEE Conf. Decision and Control 6609–6616 (IEEE, 2015). Goodfellow, I., Warde-...
2.3Trained Neural Networks and MILP Already trained neural networks and mixed integer linear programming have been brought successfully together in the past. Note, that this work is focussing on the direct optimization of the network weights and its parameters from training data with a close connectio...
In addition, the energy function of (3) is a quadratic convex function in a neighborhood of the optimal solutions, and thus the network can converge faster than that of (2). The projection technique is further used to construct neural network for solving extended linear programming problems ...
In this paper linear neural network was applied to adaptive noise cancellation technology,and the neural network was trained by least mean square (LMS) algorithm. 针对有源滤波器谐波检测实时精度高的要求,将线性神经网络应用于自适应噪声对消技术,采用最小均方(least mean square,LMS)误差算法对神经网络进...
Elsevier BVApplied EnergyCanizes B, Soares J, Faria P, Vale Z. Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets. Appl Energy 2013;108:261-70.