This paper aims to identify the current state of the art of the latest research related to Conjugate Gradient (CG) methods for unconstrained optimization through a systematic literature review according to the methodology proposed by Kitchenham and Charter, to answer the following research questions: Q1: In what research areas are the conjugate gradient method used? Q2: Can Dai-Yuan conjugate gradient algorithm be effectively applied in portfolio selection? Q3: Have conjugate gradient methods been used to develop large-scale numerical results? Q4: What conjugate gradient methods have been used to minimize quasiconvex or nonconvex functions? We obtain useful results to extend the applications of the CG methods, develop efficient algorithms, and continue studying theoretical convergence results.
|Title of host publication||Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2021|
|Event||2nd IEEE Engineering International Research Conference, EIRCON 2021 - Virtual, Lima, Peru|
Duration: 27 Oct 2021 → 29 Oct 2021
|Name||Proceedings of the 2021 IEEE Engineering International Research Conference, EIRCON 2021|
|Conference||2nd IEEE Engineering International Research Conference, EIRCON 2021|
|Period||27/10/21 → 29/10/21|
Bibliographical notePublisher Copyright:
© 2021 IEEE.
- Conjugate gradient method
- Nonconvex functions
- Optimization problems
- Quasiconvex functions