ArticlePloS one2023
The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- An efficient gradient-based algorithm with descent direction for unconstrained optimization with applications to image restoration and robotic motion control.PeerJ. Computer science · 2025Article
- A spectral Fletcher-Reeves conjugate gradient method with integrated strategy for unconstrained optimization and portfolio selection.PloS one · 2025Article
- Spectral-like conjugate gradient methods with sufficient descent property for vector optimization.PloS one · 2024Article
- Article
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5 authors.
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Abstract
In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is sufficiently descent independent of any line search technique. Thus, numerical experiments on some set of benchmark problems indicate that the method is promising and efficient. Furthermore, the efficiency of the proposed method is demonstrated on applications arising from arm robotic model and image restoration problems.
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