Results 21 to 30 of about 888,833 (294)
Conjugate gradient algorithms for conic functions [PDF]
, 1986 summary:The paper contains a description and an analysis of two modifications of the conjugate gradient method for unconstrained minimization which find a minimum of the conic function after a finite number of steps.
Lukšan, Ladislav
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A Conjugate Gradient-Based BPTT-Like Optimal Control Algorithm With Vehicle Dynamics Control Application [PDF]
, 2010 The paper presents a gradient-based algorithm for optimal control of nonlinear multivariable systems with control and state vectors constraints. The algorithm has a backward-in-time recurrent structure similar to the backpropagation-through-time ...
Kolmanovsky, Ilya V. +10 more
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A Multipreconditioned Conjugate Gradient Algorithm [PDF]
SIAM Journal on Matrix Analysis and Applications, 2006 We propose a generalization of the conjugate gradient method that uses multiple preconditioners, combining them automatically in an optimal way. The algorithm may be useful for domain decomposition techniques and other problems in which the need for more than one preconditioner arises naturally.
Robert Bridson, Chen Greif
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Moving force identification based on modified preconditioned conjugate gradient method [PDF]
, 2018 This paper develops a modified preconditioned conjugate gradient (M-PCG) method for moving force identification (MFI) by improving the conjugate gradient (CG) and preconditioned conjugate gradient (PCG) methods with a modified Gram-Schmidt algorithm. The
Zhen Chen +5 more
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International Journal of Biomedical Imaging, 2007 Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular ...
Shang Shang +4 more
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Conjugate Gradients for Kernel Machines
CoRR, 2019 Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets require to resort to approximations.
Bartels, S., Hennig, P.
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A NEW THREE -TERM CONJUGATE GRADIENT ALGORITHM FOR SOLVING MINIMIZATION PROBLEMS
Science Journal of University of Zakho, 2023 The method of optimization is used to determine the most precise value for certain functions within a certain domain; it is mostly studied and employed in the fields of mathematics, computer science, and physics.
Dilovan H. Omar +2 more
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Conjugate gradient Mojette reconstruction [PDF]
SPIE Proceedings, 2005 Iterative methods are now recognized as powerful tools to solve inverse problems such as tomographic reconstruction. In this paper, the main goal is to present a new reconstruction algorithm made from two components. An iterative algorithm, namely the Conjugate Gradient (CG) method, is used to solve the tomographic problem in the least square (LS ...
Servières, Myriam +3 more
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Two new spectral conjugate gradient algorithms based on Hestenes–Stiefel
Journal of Algorithms & Computational Technology, 2017 The spectral conjugate gradient algorithm, which is a variant of conjugate gradient method, is one of the effective methods for solving unconstrained optimization problems.
Guofang Wang +4 more
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Iteration-fusing conjugate gradient [PDF]
Proceedings of the International Conference on Supercomputing, 2017 Peer ...
Zhuang, Sicong, Casas, Marc
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