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A common practical problem when implementing signal model-fitting procedures is that of the frequency local minima. Unfortunately, conventional optimization methods, like Steepest Descent, Newton's Method and Conjugate Gradients (CG) are subject to this problem. What is worse, if the estimated frequency is not correct, the estimated signal amplitude and decay rate will be incorrect. In this paper,...
Most existing work uses dual decomposition and subgradient methods to solve Network Utility Maximization (NUM) problems in a distributed manner, which suffer from slow rate of convergence properties. This work develops an alternative distributed Newton-type fast converging algorithm for solving network utility maximization problems with self-concordant utility functions. By using novel matrix splitting...
Image registration based on gradient and least square optimization technique is one of the most edge-cutting registration algorithms. Such method, especially useful for sub-pixel motion, searches for the best motion in an iterative way. This paper solves the same motion registration problem following this direction. And the well-known Gauss-Newton method (GNM) is employed here as the optimization...
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