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The modified PRP conjugate gradient algorithm under a non-descent line search and its application in the Muskingum model and image restoration problems

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Abstract

In this paper, a modified Polak–Ribière–Polyak (PRP) method, which possesses the following desired properties for unconstrained optimization problems, is presented. (i) The search direction of the given method has the gradient value and the function value. (ii) A non-descent backtracking-type line search technique is proposed to obtain the step size \(\alpha _k\) and construct a point. (iii) The method inherits an important property of the classical PRP method: the tendency to turn towards the steepest descent direction if a small step is generated away from the solution, preventing a sequence of tiny steps from happening. (iv) The strongly global convergence and R-linear convergence of the modified PRP method for nonconvex optimization are established under some suitable assumptions. (v) The numerical results show that the modified PRP method not only is interesting in practical computation but also has better performance than the normal PRP method in estimating the parameters of the nonlinear Muskingum model and performing image restoration.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 11661009), the High Level Innovation Teams and Excellent Scholars Program in Guangxi institutions of higher education (Grant No. [2019]52)), the Guangxi Natural Science Key Fund (No. 2017GXNSFDA198046), and the Special Funds for Local Science and Technology Development Guided by the Central Government (No. ZY20198003).

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The authors declare that there no conflicts of interest regarding the publication of this paper.

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  1. College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi, People’s Republic of China

    Gonglin Yuan, Junyu Lu & Zhan Wang

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  1. Gonglin Yuan
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  2. Junyu Lu
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  3. Zhan Wang
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Correspondence to Gonglin Yuan.

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This work was supported by the National Natural Science Foundation of China (Grant No. 11661009), the High Level Innovation Teams and Excellent Scholars Program in Guangxi institutions of higher education (Grant No. [2019]52), the Guangxi Natural Science Foundation (2020GXNSFAA159069) and the Guangxi Natural Science Key Fund (No. 2017GXNSFDA198046).

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Yuan, G., Lu, J. & Wang, Z. The modified PRP conjugate gradient algorithm under a non-descent line search and its application in the Muskingum model and image restoration problems. Soft Comput 25, 5867–5879 (2021). https://doi.org/10.1007/s00500-021-05580-0

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