The nonlinear conjugate gradient method is a very useful technique for solving large scale minimization problems and has wide applications in many fields. In this paper, we present a new algorithm of ...
Abstract: Tukey's biweight M-estimate conjugate gradient method (TbMCG) is one of the best algorithms to identify the systems with colored input in an impulsive noise environment. In this algorithm, ...
Abstract: A stochastic conjugate gradient algorithm (SCGA) is proposed for the solving of the nonlinear optimization problem associated with the multiuser constant modulus algorithm (CC-CMA) for ...
In this paper, we presented a new three-term conjugate gradient method based on combining the conjugate gradient method proposed by Cheng et al [15] with the idea of the modified FR method [22]. In ...
This is a PyTorch based machine learning project that focuses on implementing the Trust Region Newton Conjugate Gradient (TRNCG) optimization algorithm to train a neural network. Since TRNCG is not ...
There are several optimization techniques available in PROC NLMIXED. You can choose a particular optimizer with the TECH=name option in the PROC NLMIXED statement. No algorithm for optimizing general ...
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