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Adjustment of the scaling parameter of Dai-Kou type conjugate gradient methods with application to motion control

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Abstract

We introduce a new scaling parameter for the Dai-Kou family of conjugate gradient algorithms (2013), which is one of the most numerically efficient methods for unconstrained optimization. The suggested parameter is based on eigenvalue analysis of the search direction matrix and minimizing the measure function defined by Dennis and Wolkowicz (1993). The corresponding search direction of conjugate gradient method has the sufficient descent property and the extended conjugacy condition. The global convergence of the proposed algorithm is given for both uniformly convex and general nonlinear objective functions. Also, numerical experiments on a set of test functions of the CUTER collections and the practical problem of the manipulator of robot movement control show that the proposed method is effective.

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Authors and Affiliations

  1. Department of Mathematics, Payame Noor University, Nakhl St, Lashkarak Highway, Tehran, 19395-4697, Iran

    Mahbube Akbari, Saeed Nezhadhosein & Aghile Heydari

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  1. Mahbube Akbari
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  2. Saeed Nezhadhosein
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  3. Aghile Heydari
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Correspondence to Saeed Nezhadhosein.

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Akbari, M., Nezhadhosein, S. & Heydari, A. Adjustment of the scaling parameter of Dai-Kou type conjugate gradient methods with application to motion control. Appl Math (2024). https://doi.org/10.21136/AM.2024.0006-24

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  • DOI: https://doi.org/10.21136/AM.2024.0006-24

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