A Newton-Like Trust Region Method for Large-Scale Unconstrained Nonconvex Minimization
Keyword(s):
We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new straightforward limited memory quasi-Newton updating based on the modified quasi-Newton equation is deduced to construct the trust region subproblem, in which the information of both the function value and gradient is used to construct approximate Hessian. The global convergence of the algorithm is proved. Numerical results indicate that the proposed method is competitive and efficient on some classical large-scale nonconvex test problems.
2014 ◽
Vol 19
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pp. 469-490
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2016 ◽
Vol 303
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pp. 105-118
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2000 ◽
Vol 16
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pp. 320-328
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2021 ◽
pp. 2150053
2019 ◽
Vol 12
(3)
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pp. 389-399
1997 ◽
Vol 66
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pp. 1509-1521
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2019 ◽
Vol 74
(3)
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pp. 669-701
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