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Modifying Spectral Conjugate Gradient Method for Solving Unconstrained Optimization Problems

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The proposed spectral conjugate gradient method reduces iteration counts and evaluations, enhancing convergence efficiency.
Numerical experiments show superior performance compared to classical methods in fast convergence and low evaluation cost.
Theoretical analysis guarantees global convergence under standard assumptions, ensuring method reliability.
Integration of modified spectral coefficients improves direction quality and maintains step stability during optimization.

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The proposed method enhances convergence efficiency in unconstrained optimization, suggesting improved iterations and outcomes.

Murad et al. (2025) studied this question.

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