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An Enhanced Conjugate Gradient Method for Nonlinear Minimization Problems

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The proposed method enhances convergence properties for nonlinear optimization problems, boosting effectiveness.
Global convergence qualities were proven under standard assumptions, ensuring robustness of the new technique.
Numerical tests indicated the new method outperforms traditional Hestenes-Stiefel in function evaluations and iterations.
The study emphasizes efficiency with minimal memory requirements, making it suitable for large-scale optimization tasks.

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New conjugate gradient technique improves convergence in nonlinear optimization, suggesting enhanced efficiency.

Mustafa et al. (2025) studied this question.

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