Standard
Some Methods for Minimizing of d.c. Functions. / Polyakova, L. ; Karelin, V. ; Myshkov, S. ; Stankova, E. .
Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV. ред. / Sanjay Misra; et al. Springer Nature, 2019. стр. 667–677 (LNCS; Том 11622 ).
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Harvard
Polyakova, L, Karelin, V, Myshkov, S & Stankova, E 2019,
Some Methods for Minimizing of d.c. Functions. в S Misra & EA (ред.),
Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV. LNCS, Том. 11622 , Springer Nature, стр. 667–677, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Российская Федерация,
1/07/19.
https://doi.org/10.1007/978-3-030-24305-0_49
APA
Polyakova, L., Karelin, V., Myshkov, S., & Stankova, E. (2019).
Some Methods for Minimizing of d.c. Functions. в S. Misra, & E. A. (Ред.),
Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV (стр. 667–677). (LNCS; Том 11622 ). Springer Nature.
https://doi.org/10.1007/978-3-030-24305-0_49
Vancouver
Polyakova L, Karelin V, Myshkov S, Stankova E.
Some Methods for Minimizing of d.c. Functions. в Misra S, EA, Редакторы, Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV. Springer Nature. 2019. стр. 667–677. (LNCS).
https://doi.org/10.1007/978-3-030-24305-0_49
Author
Polyakova, L. ; Karelin, V. ; Myshkov, S. ; Stankova, E. . /
Some Methods for Minimizing of d.c. Functions. Computational Science and Its Applications – ICCSA 2019: 19th International Conference, Saint Petersburg, Russia, July 1–4, 2019, Proceedings, Part IV. Редактор / Sanjay Misra ; et al. Springer Nature, 2019. стр. 667–677 (LNCS).
BibTeX
@inproceedings{defa0e8114d54bf69c4bd2c519da810c,
title = "Some Methods for Minimizing of d.c. Functions",
abstract = "In the paper, we present algorithms for minimization of d.c. functions (difference of two convex functions) on the whole space$$R^n$$. Many nonconvex optimization problems can be described using these functions. D.c. functions are used in various applications especially in optimization, but the problem to characterize them is not trivial, due to the fact that these functions are not differentiable and certainly are not convex. The class of these functions is contained in the class of quasidifferentiable functions. Proposed algorithms are based on known necessary optimality conditions and d.c. duality. Convergence to$$\inf $$ -stationary points is established under fairly general natural assumptions.",
keywords = "Convex function, Difference of convex functions (d.c. functions), Quasidifferentiable functions, Subdifferential",
author = "L. Polyakova and V. Karelin and S. Myshkov and E. Stankova",
note = "Polyakova L., Karelin V., Myshkov S., Stankova E. (2019) Some Methods for Minimizing of d.c. Functions. In: Misra S. et al. (eds) Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science, vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_49; 19th International Conference on Computational Science and Its Applications, ICCSA 2019 ; Conference date: 01-07-2019 Through 04-07-2019",
year = "2019",
doi = "10.1007/978-3-030-24305-0_49",
language = "English",
isbn = "9783030243043",
series = "LNCS",
publisher = "Springer Nature",
pages = "667–677",
editor = "Sanjay Misra and {et al.}",
booktitle = "Computational Science and Its Applications – ICCSA 2019",
address = "Germany",
}
RIS
TY - GEN
T1 - Some Methods for Minimizing of d.c. Functions
AU - Polyakova, L.
AU - Karelin, V.
AU - Myshkov, S.
AU - Stankova, E.
N1 - Conference code: 19
PY - 2019
Y1 - 2019
N2 - In the paper, we present algorithms for minimization of d.c. functions (difference of two convex functions) on the whole space$$R^n$$. Many nonconvex optimization problems can be described using these functions. D.c. functions are used in various applications especially in optimization, but the problem to characterize them is not trivial, due to the fact that these functions are not differentiable and certainly are not convex. The class of these functions is contained in the class of quasidifferentiable functions. Proposed algorithms are based on known necessary optimality conditions and d.c. duality. Convergence to$$\inf $$ -stationary points is established under fairly general natural assumptions.
AB - In the paper, we present algorithms for minimization of d.c. functions (difference of two convex functions) on the whole space$$R^n$$. Many nonconvex optimization problems can be described using these functions. D.c. functions are used in various applications especially in optimization, but the problem to characterize them is not trivial, due to the fact that these functions are not differentiable and certainly are not convex. The class of these functions is contained in the class of quasidifferentiable functions. Proposed algorithms are based on known necessary optimality conditions and d.c. duality. Convergence to$$\inf $$ -stationary points is established under fairly general natural assumptions.
KW - Convex function
KW - Difference of convex functions (d.c. functions)
KW - Quasidifferentiable functions
KW - Subdifferential
UR - http://www.scopus.com/inward/record.url?scp=85068622369&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-24305-0_49
DO - 10.1007/978-3-030-24305-0_49
M3 - Conference contribution
SN - 9783030243043
T3 - LNCS
SP - 667
EP - 677
BT - Computational Science and Its Applications – ICCSA 2019
A2 - Misra, Sanjay
A2 - null, et al.
PB - Springer Nature
T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019
Y2 - 1 July 2019 through 4 July 2019
ER -