Error Bounds for Piecewise Convex Quadratic Programs and Applications
SIAM Journal on Control and Optimization
Inexact SQP Interior Point Methods and Large Scale Optimal Control Problems
SIAM Journal on Control and Optimization
Robust Control via Sequential Semidefinite Programming
SIAM Journal on Control and Optimization
A New Algorithm for Solving Strictly Convex Quadratic Programs
SIAM Journal on Optimization
New Results on Quadratic Minimization
SIAM Journal on Optimization
Convex Optimization
Approximation Bounds for Quadratic Optimization with Homogeneous Quadratic Constraints
SIAM Journal on Optimization
Subgradient-based neural networks for nonsmooth nonconvex optimization problems
IEEE Transactions on Neural Networks
Time-varying two-phase optimization and its application to neural-network learning
IEEE Transactions on Neural Networks
A recurrent neural network for solving Sylvester equation with time-varying coefficients
IEEE Transactions on Neural Networks
Design and analysis of a general recurrent neural network model for time-varying matrix inversion
IEEE Transactions on Neural Networks
Linear and quadratic programming neural network analysis
IEEE Transactions on Neural Networks
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In this paper, the performance of a gradient neural network (GNN), which was designed intrinsically for solving static problems, is investigated, analyzed and simulated in the situation of time-varying coefficients. It is theoretically proved that the gradient neural network for online solution of time-varying quadratic minimization (QM) and quadratic programming (QP) problems could only approximately approach the time-varying theoretical solution, instead of converging exactly. That is, the steady-state error between the GNN solution and the theoretical solution can not decrease to zero. In order to understand the situation better, the upper bound of such an error is estimated firstly, and then the global exponential convergence rate is investigated for such a GNN when approaching an error bound. Computer-simulation results, including those based on a six-link robot manipulator, further substantiate the performance analysis of the GNN exploited to solve online time-varying QM and QP problems.