Standard

Optimality Conditions in Terms of Alternance: Two Approaches. / Demyanov, V.F.; Malozemov, V.N.

в: Journal of Optimization Theory and Applications, Том 162, № 3, 2014, стр. 805-820.

Результаты исследований: Научные публикации в периодических изданияхстатья

Harvard

Demyanov, VF & Malozemov, VN 2014, 'Optimality Conditions in Terms of Alternance: Two Approaches', Journal of Optimization Theory and Applications, Том. 162, № 3, стр. 805-820. https://doi.org/10.1007/s10957-013-0472-8

APA

Demyanov, V. F., & Malozemov, V. N. (2014). Optimality Conditions in Terms of Alternance: Two Approaches. Journal of Optimization Theory and Applications, 162(3), 805-820. https://doi.org/10.1007/s10957-013-0472-8

Vancouver

Demyanov VF, Malozemov VN. Optimality Conditions in Terms of Alternance: Two Approaches. Journal of Optimization Theory and Applications. 2014;162(3):805-820. https://doi.org/10.1007/s10957-013-0472-8

Author

Demyanov, V.F. ; Malozemov, V.N. / Optimality Conditions in Terms of Alternance: Two Approaches. в: Journal of Optimization Theory and Applications. 2014 ; Том 162, № 3. стр. 805-820.

BibTeX

@article{1f0aea7ab3b240e0bf6da7c064727258,
title = "Optimality Conditions in Terms of Alternance: Two Approaches",
abstract = "In Optimization Theory, necessary and sufficient optimality conditions play an essential role. They allow, first of all, checking whether a point under study satisfies the conditions, and, secondly, if it does not, finding a {"}better{"} point. For the class of directionally differentiable functions, a necessary condition for an unconstrained minimum requires the directional derivative to be non-negative in all directions. This condition becomes efficient for special classes of directionally differentiable functions. For example, in the case of convex and max-type functions, the necessary condition for a minimum takes the form of inclusion. The problem of verifying this condition is reduced to that of finding the point of some convex and compact set C which is nearest to the origin. If the origin does not belong to C, we easily find the steepest descent direction, and are able to construct a numerical method. In the classical Chebyshev polynomial approximation problem, necessary optimality conditions are expresse",
author = "V.F. Demyanov and V.N. Malozemov",
year = "2014",
doi = "10.1007/s10957-013-0472-8",
language = "English",
volume = "162",
pages = "805--820",
journal = "Journal of Optimization Theory and Applications",
issn = "0022-3239",
publisher = "Springer Nature",
number = "3",

}

RIS

TY - JOUR

T1 - Optimality Conditions in Terms of Alternance: Two Approaches

AU - Demyanov, V.F.

AU - Malozemov, V.N.

PY - 2014

Y1 - 2014

N2 - In Optimization Theory, necessary and sufficient optimality conditions play an essential role. They allow, first of all, checking whether a point under study satisfies the conditions, and, secondly, if it does not, finding a "better" point. For the class of directionally differentiable functions, a necessary condition for an unconstrained minimum requires the directional derivative to be non-negative in all directions. This condition becomes efficient for special classes of directionally differentiable functions. For example, in the case of convex and max-type functions, the necessary condition for a minimum takes the form of inclusion. The problem of verifying this condition is reduced to that of finding the point of some convex and compact set C which is nearest to the origin. If the origin does not belong to C, we easily find the steepest descent direction, and are able to construct a numerical method. In the classical Chebyshev polynomial approximation problem, necessary optimality conditions are expresse

AB - In Optimization Theory, necessary and sufficient optimality conditions play an essential role. They allow, first of all, checking whether a point under study satisfies the conditions, and, secondly, if it does not, finding a "better" point. For the class of directionally differentiable functions, a necessary condition for an unconstrained minimum requires the directional derivative to be non-negative in all directions. This condition becomes efficient for special classes of directionally differentiable functions. For example, in the case of convex and max-type functions, the necessary condition for a minimum takes the form of inclusion. The problem of verifying this condition is reduced to that of finding the point of some convex and compact set C which is nearest to the origin. If the origin does not belong to C, we easily find the steepest descent direction, and are able to construct a numerical method. In the classical Chebyshev polynomial approximation problem, necessary optimality conditions are expresse

U2 - 10.1007/s10957-013-0472-8

DO - 10.1007/s10957-013-0472-8

M3 - Article

VL - 162

SP - 805

EP - 820

JO - Journal of Optimization Theory and Applications

JF - Journal of Optimization Theory and Applications

SN - 0022-3239

IS - 3

ER -

ID: 7020688