Savku, Emel

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/10448
Name Variants
S., Emel
Savku, E.
E., Savku
Emel, Savku
Savku, Emel
Emel, S.
Job Title
Dr. Öğr. Üyesi
Email Address
emel.savku@atilim.edu.tr
Main Affiliation
Computer Engineering
Status
Current Staff
Website
ORCID ID
0000-0001-8731-2928
Scopus Author ID
57195469894
Turkish CoHE Profile ID
Google Scholar ID
DELrcDIAAAAJ
WoS Researcher ID
ABH-2731-2021

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Documents

10

Citations

281

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5

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Documents

8

Citations

268

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Scholarly Output

3

Articles

3

Views / Downloads

36/196

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

6

Scopus Citation Count

5

WoS h-index

2

Scopus h-index

1

Patents

0

Projects

0

WoS Citations per Publication

2.00

Scopus Citations per Publication

1.67

Open Access Source

2

Supervised Theses

0

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JournalCount
Communications Faculty of Sciences University of Ankara Series A1: Mathematics and Statistics1
Optimization1
Stochastics1
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Citation - WoS: 4
    Citation - Scopus: 4
    An Approach for Regime-Switching Stochastic Control Problems With Memory and Terminal Conditions
    (Taylor & Francis Ltd, 2024) Savku, E.
    In this research article, we focus on a stochastic optimal control problem with two types of terminal constraints. These specific conditions provide real-valued and stochastic Lagrange multipliers. Our model evolves according to a Markov regime-switching jump diffusion model with memory. In this context, the memory is represented by a Stochastic Differential Delay Equation. We present two theorems for each constraint within the general formulation of stochastic optimal control theory in a Lagrangian environment. We approach to this task from a theoretical perspective and provide mild technical assumptions, which make our theorems applicable for a broad class of stochastic control problems as well as for a wide range of disciplines such as engineering, biology, operations research, medicine, computer science and economics. In this work, we apply Stochastic Maximum Principle to demonstrate an optimal dividend policy corresponding to a time-delayed wealth process of a company. Moreover, we determine the real-valued Lagrange multiplier of this control problem explicitly.
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 1
    Memory and Anticipation: Two Main Theorems for Markov Regime-Switching Stochastic Processes
    (Taylor & Francis Ltd, 2025) Savku, E.
    We present two main theorems for stochastic processes with a Markov regime-switching model. First, we work on an existence-uniqueness theorem for a Stochastic Differential Delay Equation with Jumps and Regimes (SDDEJRs). Then we provide the duality between an SDDEJR and an Anticipated Backward Stochastic Differential Equation with Jumps and Regimes (ABSDEJRs). Our goal is to provide two technical and fundamental theorems for the future theoretical and applied developments of time-delayed and time-advanced models.
  • Thumbnail Image
    Article
    An Application of Stochastic Maximum Principle for a Constrained System With Memory
    (Ankara Univ, Fac Sci, 2025) Savku, Emel
    In this research article, we study a stochastic control problem in a theoretical frame to solve a constrained task under memory impact. The nature of memory is modeled by Stochastic Differential Delay Equations and our state process evolves according to a jump-diffusion process with time-delay. We work on two specific types of constraints, which are described in the stochastic control problem as running gain components. We develop two theorems for corresponding deterministic and stochastic Lagrange multipliers. Furthermore, these theorems are applicable to a wide range of continuous-time stochastic optimal control problems in a diversified scientific area such as Operations Research, Biology, Computer Science, Engineering and Finance. Here, in this work, we apply our results to a financial application to investigate the optimal consumption process of a company via its wealth process with historical performance. We utilize the stochastic maximum principle, which is one of the main methods of continuous-time Stochastic Optimal Control theory. Moreover, we compute a real-valued Lagrange multiplier and clarify the relation between this value and the specified constraint.