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Volumn 27, Issue 1, 2017, Pages 478-506

Regularized Newton methods for minimizing functions with Hölder continuous Hessians

Author keywords

H lder condition; Second order methods; Unconstrained minimization; Worst case global complexity bounds

Indexed keywords

COMPUTER SCIENCE; SOFTWARE ENGINEERING;

EID: 85021161662     PISSN: 10526234     EISSN: None     Source Type: Journal    
DOI: 10.1137/16M1087801     Document Type: Article
Times cited : (84)

References (10)
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    • F.E. Curtis, D.P. Robinson, and M. Samadi, A trust-region algorithm with a worst-case iteration complexity of O(l-3/2) for nonconvex optimization, Math. Program., 162 (2017), pp. 1-32.
    • (2017) Math. Program. , vol.162 , pp. 1-32
    • Curtis, F.E.1    Robinson, D.P.2    Samadi, M.3
  • 6
    • 84991328710 scopus 로고    scopus 로고
    • Cubic-regularization counterpart of a variable-norm trustregion method for unconstrained minimization
    • J.M. Martínez, and M. Raydan, Cubic-regularization counterpart of a variable-norm trustregion method for unconstrained minimization, J. Global Optim., https://doi.org/10.1007/s10898-016-0475-8 (2016).
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  • 7
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  • 8
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    • Nesterov, Yu.1
  • 9
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    • Yu. Nesterov, Universal gradient methods for convex optimization problems, Math. Program., 152 (2015), pp. 381-404.
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    • Cubic regularization of Newton's method and its global performance
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.