A hybrid IFR-IDY conjugate gradient algorithm for unconstrained optimization and its application in portfolio selection

Diva Marchandra Mulansari; Maulana Malik; Sindy Devila; Ibrahim Mohammed Sulaiman; Dian Lestari; Fevi Novkaniza; Fida Fathiyah Addini

doi:10.46481/jnsps.2026.3105

Authors

Diva Marchandra Mulansari
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Maulana Malik
[email protected]

Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Sindy Devila
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Ibrahim Mohammed Sulaiman
College of Applied and Health Sciences, A’Sharqiyah University, Ibra 400, Sultanate of Oman
Dian Lestari
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Fevi Novkaniza
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia
Fida Fathiyah Addini
Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok 16424, Indonesia

Keywords:

Inexact line search, Global convergence, Investment, Numerical optimization, Strong Wolfe

Abstract

This study introduces the Improved Fletcher-Reeves (IFR)-Improved Dai-Yuan (IDY) hybrid conjugate gradient method, which combines the strengths of the IFR and IDY parameters through a minimum-operator strategy to enhance robustness in unconstrained optimization. The method is shown to satisfy descent and global convergence properties under the strong Wolfe line search. Numerical experiments on 134 benchmark functions demonstrate that IFR-IDY achieves superior performance, solving 98 problems more than IFR and IDY and exhibiting faster CPU times and fewer iterations in most cases. The method is also used to solve an IDX30 portfolio optimization problem, which results in an optimal allocation with an expected return of 0.00042 and a risk of 0.000050545. These results highlight the efficiency of IFR-IDY and its practical applicability in real-world decision-making.

Dimensions

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