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A spectral conjugate gradient algorithm for vector optimization

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The aim is to develop a modified spectral conjugate gradient algorithm for vector optimization problems without convexity requirements.
Introduced a spectral scaling mechanism that maintains sufficient descent without requiring exact line searches.
Developed a new update rule for the conjugate parameter within a flexible spectral framework.
Conducted numerical experiments on benchmark problems to compare performance against existing methods.
Demonstrated global convergence for both the general method and the proposed variant under Wolfe-type conditions.
Numerical tests indicated that the new algorithm is computationally competitive compared to traditional conjugate gradient methods.

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Randomized trial shows improved vector optimization methods, indicating enhanced computational efficiency.

Peng et al. (2026) studied this question.

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