Bakan, Hacer Öz

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Profile URL
https://hdl.handle.net/20.500.14411/8027
Name Variants
Hacer Öz, Bakan
H., Bakan
Bakan, Hacer Oz
B.,Hacer Oz
B., Hacer Oz
Bakan,H.O.
Bakan,H.Ö.
H.,Bakan
Bakan, Hacer Öz
H.Ö.Bakan
B.,Hacer Öz
H.O.Bakan
Hacer Oz, Bakan
Öz Bakan,H.
Job Title
Araştırma Görevlisi
Email Address
hacer.oz@atilim.edu.tr
Main Affiliation
Mathematics
Status
Former Staff
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ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

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Scholarly Output

3

Articles

2

Views / Downloads

3/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

14

Scopus Citation Count

18

Patents

0

Projects

0

WoS Citations per Publication

4.67

Scopus Citations per Publication

6.00

Open Access Source

1

Supervised Theses

0

JournalCount
Computational Management Science1
Journal of Computational and Applied Mathematics1
Springer Proceedings in Mathematics and Statistics -- 3rd International Conference on Dynamics, Games and Science, DGS 2014 -- 17 February 2014 through 21 February 2014 -- Porto -- 1998791
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2017 - 20182
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Type: Article

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Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - WoS: 3
    Citation - Scopus: 6
    A Discrete Optimality System for an Optimal Harvesting Problem
    (Springer Heidelberg, 2017) Bakan, Hacer Oz; Yilmaz, Fikriye; Weber, Gerhard-Wilhelm; Öz Bakan, Hacer
    In this paper, we obtain the discrete optimality system of an optimal harvesting problem. While maximizing a combination of the total expected utility of the consumption and of the terminal size of a population, as a dynamic constraint, we assume that the density of the population is modeled by a stochastic quasi-linear heat equation. Finite-difference and symplectic partitioned Runge-Kutta (SPRK) schemes are used for space and time discretizations, respectively. It is the first time that a SPRK scheme is employed for the optimal control of stochastic partial differential equations. Monte-Carlo simulation is applied to handle expectation appearing in the cost functional. We present our results together with a numerical example. The paper ends with a conclusion and an outlook to future studies, on further research questions and applications.
  • Thumbnail Image
    Article
    Citation - WoS: 11
    Citation - Scopus: 12
    Minimal Truncation Error Constants for Runge-Kutta Method for Stochastic Optimal Control Problems
    (Elsevier, 2018) Bakan, Hacer Oz; Bakan, Hacer Öz; Yilmaz, Fikriye; Weber, Gerhard-Wilhelm; Bakan, Hacer Öz; Mathematics; Mathematics
    In this work, we obtain strong order-1 conditions with minimal truncation error constants of Runge-Kutta method for the optimal control of stochastic differential equations (SDEs). We match Stratonovich-Taylor expansion of the exact solution with Stratonovich-Taylor expansion of our approximation method that is defined by the Runge-Kutta scheme, term by term, in order to get the strong order-1 conditions. By a conclusion and an outlook to future research, the paper ends. (C) 2017 Elsevier B.V. All rights reserved.