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A Modified Hybrid Conjugate Gradient Method for Unconstrained Optimization [PDF]
Journal of Mathematics, 2021 The nonlinear conjugate gradient algorithms are a very effective way in solving large-scale unconstrained optimization problems. Based on some famous previous conjugate gradient methods, a modified hybrid conjugate gradient method was proposed.
Minglei Fang +3 more
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The conjugate gradient method [PDF]
The Leading Edge, 2018 The conjugate gradient method can be used to solve many large linear geophysical problems — for example, least-squares parabolic and hyperbolic Radon transform, traveltime tomography, least-squares migration, and full-waveform inversion (FWI) (e.g., Witte et al., 2018).
Karl Schleicher
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The Limited Memory Conjugate Gradient Method [PDF]
SIAM Journal on Optimization, 2013 In theory, the successive gradients generated by the conjugate gradient method applied to a quadratic should be orthogonal. However, for some ill-conditioned problems, orthogonality is quickly lost due to rounding errors, and convergence is much slower than expected. A limited memory version of the nonlinear conjugate gradient method is developed.
William W. Hager, Hongchao Zhang
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A new modified conjugate gradient method under the strong Wolfe line search for solving unconstrained optimization problems [PDF]
, 2022 Conjugate gradient (CG) method is well-known due to efficiency to solve the problems of unconstrained optimization because of its convergence properties and low computation cost.
June, L.W., Ishak, M. I., Marjugi, S.M.
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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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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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Computing several eigenpairs of Hermitian problems by conjugate gradient iterations [PDF]
, 2008 The paper is concerned with algorithms for computing several extreme eigenpairs of Hermitian problems based on the conjugate gradient method. We analyse computational strategies employed by various algorithms of this kind reported in the literature and ...
Ovtchinnikov, E.
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On Meinardus’ examples for the conjugate gradient method [PDF]
Mathematics of Computation, 2007 The conjugate gradient (CG) method is widely used to solve a positive definite linear system A x = b
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Spectral CG Algorithm for Solving Fuzzy Non-linear Equations
Iraqi Journal for Computer Science and Mathematics, 2022 The non-linear conjugate gradient method is a very effective technique for addressing Large-Scale minimization problems, and it has a wide range of applications in Mathematics, Chemistry, Physics, Engineering, and Medicine, etc.
Mezher M. Abed +2 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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