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Nonmonotone conjugate gradient methods for optimization
1994In this paper conjugate gradient methods with nonmonotone line search technique are introduced. This new line search technique is based on a relaxation of the strong Wolfe conditions and it allows to accept larger steps. The proposed conjugate gradient methods are still globally convergent and, at the same time, they should not suffer the propensity ...
LUCIDI, Stefano, ROMA, Massimo
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Optimizing a Parallel Conjugate Gradient Solver
SIAM Journal on Scientific Computing, 1998Interesting report about the tuning of a classical (symmetric) conjugate gradient code for sparse matrices. At first the matrix-vector multiplication is tuned for a single processor (type IBM RS/6000), then for a parallel computer Hitachi SR4300 with up to 32 processors. Significant improvements compared to the naive code are obtained.
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Conjugate Gradient Methods with Inexact Searches
Mathematics of Operations Research, 1978Conjugate gradient methods are iterative methods for finding the minimizer of a scalar function f(x) of a vector variable x which do not update an approximation to the inverse Hessian matrix. This paper examines the effects of inexact linear searches on the methods and shows how the traditional Fletcher-Reeves and Polak-Ribiere algorithm may be ...
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The Dai–Liao nonlinear conjugate gradient method with optimal parameter choices
European Journal of Operational Research, 2014Saman Babaie-Kafaki, Reza Ghanbari
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A Nonlinear Conjugate Gradient Algorithm with an Optimal Property and an Improved Wolfe Line Search
SIAM Journal on Optimization, 2013Yu-Hong Dai
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A new, globally convergent Riemannian conjugate gradient method
Optimization, 2015Hiroyuki Sato
exaly