Details
| Originalsprache | Englisch |
|---|---|
| Fachzeitschrift | OPTIMIZATION |
| Frühes Online-Datum | 11 Sept. 2023 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 11 Sept. 2023 |
Abstract
The paper deals with fractional optimization problems where the objective function (ratio of two functions) is defined on a Cartesian product of two real normed spaces X and Y. Within this framework, we are interested to determine the so-called partial minimizers, i.e. points in (Formula presented.) with the property that any of its variables minimizes the objective function, restricted to this variable, with respect to the other one. While any global minimizer is obviously a partial minimizer, the reverse implication holds true only under additional assumptions (e.g. separability properties of the involved functions). By exploiting the particularities of the objective function, we deliver a Dinkelbach type algorithm for computing partial minimizers of fractional optimization problems. Further assumptions on the involved spaces and functions, such as Lipschitz-type continuity, partial Fréchet differentiability, and coercivity, enable us to establish the convergence of our algorithm to a partial minimizer.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
- Mathematik (insg.)
- Angewandte Mathematik
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in: OPTIMIZATION, 11.09.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Componentwise Dinkelbach algorithm for nonlinear fractional optimization problems
AU - Günther, Christian
AU - Orzan, Alexandru
AU - Precup, Radu
PY - 2023/9/11
Y1 - 2023/9/11
N2 - The paper deals with fractional optimization problems where the objective function (ratio of two functions) is defined on a Cartesian product of two real normed spaces X and Y. Within this framework, we are interested to determine the so-called partial minimizers, i.e. points in (Formula presented.) with the property that any of its variables minimizes the objective function, restricted to this variable, with respect to the other one. While any global minimizer is obviously a partial minimizer, the reverse implication holds true only under additional assumptions (e.g. separability properties of the involved functions). By exploiting the particularities of the objective function, we deliver a Dinkelbach type algorithm for computing partial minimizers of fractional optimization problems. Further assumptions on the involved spaces and functions, such as Lipschitz-type continuity, partial Fréchet differentiability, and coercivity, enable us to establish the convergence of our algorithm to a partial minimizer.
AB - The paper deals with fractional optimization problems where the objective function (ratio of two functions) is defined on a Cartesian product of two real normed spaces X and Y. Within this framework, we are interested to determine the so-called partial minimizers, i.e. points in (Formula presented.) with the property that any of its variables minimizes the objective function, restricted to this variable, with respect to the other one. While any global minimizer is obviously a partial minimizer, the reverse implication holds true only under additional assumptions (e.g. separability properties of the involved functions). By exploiting the particularities of the objective function, we deliver a Dinkelbach type algorithm for computing partial minimizers of fractional optimization problems. Further assumptions on the involved spaces and functions, such as Lipschitz-type continuity, partial Fréchet differentiability, and coercivity, enable us to establish the convergence of our algorithm to a partial minimizer.
KW - coercive function
KW - Dinkelbach type algorithm
KW - Fractional optimization
KW - partial minimizer
UR - http://www.scopus.com/inward/record.url?scp=85170687202&partnerID=8YFLogxK
U2 - 10.1080/02331934.2023.2256750
DO - 10.1080/02331934.2023.2256750
M3 - Article
AN - SCOPUS:85170687202
JO - OPTIMIZATION
JF - OPTIMIZATION
SN - 0233-1934
ER -