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A new sufficiently descent algorithm for pseudomonotone nonlinear operator equations and signal reconstruction

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Abstract

This paper presents a new sufficiently descent algorithm for system of nonlinear equations where the underlying operator is pseudomonotone. The conditions imposed on the proposed algorithm to achieve convergence are Lipschitz continuity and pseudomonotonicity which is weaker than monotonicity assumption forced upon many algorithms in this area found in the literature. Numerical experiments on selected test problems taken from the literature validate the efficiency of the new algorithm. Moreover, the new algorithm demonstrates superior performance in comparison with some existing algorithms. Furthermore, the proposed algorithm is applied to reconstruct some disturbed signals.

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Data Availability

The MATLAB codes for the implementation of the proposed algorithm are available upon request.

References

  1. Sulaiman, I.M., Awwal, A.M., Malik, M., Pakkaranang, N., Panyanak, B.: A Derivative-Free MZPRP Projection Method for Convex Constrained Nonlinear Equations and Its Application in Compressive Sensing. Mathematics 10(16), 2884 (2022)

    Article  Google Scholar 

  2. Mohammad, H., Awwal, A.M.: Globally convergent diagonal Polak–Ribière–Polyak like algorithm for nonlinear equations. Numer. Algo. 1–20 (2022)

  3. Waziri, M.Y., Ahmed, K., Halilu, A.S.: A modified Dai-Kou-type method with applications to signal reconstruction and blurred image restoration. Comput. Appl. Math. 41(6), 1–33 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  4. Awwal, A.M., Kumam, P., Wang, L., Huang, S., Kumam, W.: Inertial-based derivative-free method for system of monotone nonlinear equations and application. IEEE Access 8, 226921–226930 (2020)

    Article  Google Scholar 

  5. Xiao, Y., Zhu, H.: A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J. Math. Anal. Appl. 405(1), 310–319 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  6. Liu, J.K., Li, S.J.: A projection method for convex constrained monotone nonlinear equations with applications. Comput. Math. Appl. 70(10), 2442–2453 (2015)

    MathSciNet  MATH  Google Scholar 

  7. Dai, Z.F., Ti, L., Yang, M.: Forecasting stock return volatility: The role of shrinkage approaches in a data-rich environment. J. Forecast. 1–17, (2022)

  8. Halilu, A.S., Majumder, A., Waziri, M.Y., Awwal, A.M., Ahmed, K.: On solving double direction methods for convex constrained monotone nonlinear equations with image restoration. Comput. Appl. Math. 40(7), 1–27 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  9. Dai, Z.F., Zhu, H., Zhang, X.: Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle. Energy Econ. 109, 105959 (2022)

    Article  Google Scholar 

  10. Ni, T., Liu, D., Xu, Q., Huang, Z., Liang, H., Yan, A.: Architecture of cobweb-based redundant TSV for clustered faults. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 28(7), 1736–1739 (2020)

    Article  Google Scholar 

  11. Halilu, A.S., Majumder, A., Waziri, M.Y., Ahmed, K., Awwal, A.M.: Motion control of the two joint planar robotic manipulators through accelerated Dai-Liao method for solving system of nonlinear equations. Eng. Comput. 39(5), 1802–1840 (2022)

    Article  Google Scholar 

  12. Aji, S., Kumam, P., Awwal, A.M., Yahaya, M.M., Sitthithakerngkiet, K.: An efficient DY-type spectral conjugate gradient method for system of nonlinear monotone equations with application in signal recovery. AIMS Math. 6(8), 8078–8106 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  13. Muhammad, A.B., Tammer, C., Awwal, A.M., Elster, R., Ma, Z.: Inertial-type projection methods for solving convex constrained monotone nonlinear equations with applications to robotic motion control. J. Nonlinear Variational Anal. 5(5), 831–849 (2021)

    Google Scholar 

  14. Li, D., Ge, S.S., Lee, T.H.: Fixed-time-synchronized consensus control of multiagent systems. IEEE Trans. Control. Netw. Syst. 8(1), 89–98 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  15. Aji, S., Kumam, P., Awwal, A.M., Yahaya, M.M., Kumam, W.: Two hybrid spectral methods with inertial effect for solving system of nonlinear monotone equations with application in robotics. IEEE Access 9, 30918–30928 (2021)

    Article  MATH  Google Scholar 

  16. Awwal, A.M., Wang, L., Kumam, P., Mohammad, H.: A two-step spectral gradient projection method for system of nonlinear monotone equations and image deblurring problems. Symmetry 12(6), 874 (2020)

    Article  Google Scholar 

  17. Dai, Y.-H., Liao, L.-Z.: New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43(1), 87–101 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  18. Arazm, M.R., Babaie-Kafaki, S., Ghanbari, R.: An extended Dai-Liao conjugate gradient method with global convergence for nonconvex functions. Glas. Mat. 52(2), 361–375 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  19. Babaie-Kafaki, S., Ghanbari, R.: A descent family of Dai-Liao conjugate gradient methods. Optim. Methods Softw. 29(3), 583–591 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Awwal, A.M., Kumam, P., Mohammad, H., Watthayu, W., Abubakar, A.B.: A Perry-type derivative-free algorithm for solving nonlinear system of equations and minimizing \(\ell _1\) regularized problem. Optimization 1–29 (2020)

  21. Awwal, A.M., Kumam, P., Abubakar, A.B.: A modified conjugate gradient method for monotone nonlinear equations with convex constraints. Appl. Numer. Math. 145, 507–520 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  22. Awwal, A.M., Kumam, P., Abubakar, A.B.: Spectral modified Polak-Ribiére-Polyak projection conjugate gradient method for solving monotone systems of nonlinear equations. Appl. Math. Comput. 362, 124514 (2019)

    MathSciNet  MATH  Google Scholar 

  23. Jian, J., Han, L., Jiang, X.: A hybrid conjugate gradient method with descent property for unconstrained optimization. Appl. Math. Model. 39(3–4), 1281–1290 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  24. Aminifard, Z., Babaie-Kafaki, S.: Dai-liao extensions of a descent hybrid nonlinear conjugate gradient method with application in signal processing. Numer. Algoritm. 89(3), 1369–1387 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  25. Liu, J., Li, S.: Spectral DY-type projection method for nonlinear monotone systems of equations. J. Comput. Math. 33(4), 341–355 (2015)

    Article  MathSciNet  Google Scholar 

  26. Liu, S.Y., Huang, Y.Y., Jiao, H.W.: Sufficient descent conjugate gradient methods for solving convex constrained nonlinear monotone equations. In: Abstract and Applied Analysis, vol. 2014. Hindawi, (2014)

  27. Waziri, M.Y., Ahmed, K., Sabi’u, J.: A family of Hager-Zhang conjugate gradient methods for system of monotone nonlinear equations. Appl. Math. Comput. 361, 645–660 (2019). https://doi.org/10.1016/j.amc.2019.06.012

    Article  MathSciNet  MATH  Google Scholar 

  28. Cheng, W.: A PRP type method for systems of monotone equations. Math. Comput. Model. 50(1–2), 15–20 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  29. Solodov, M.V., Svaiter, B.F.: A globally convergent inexact Newton method for systems of monotone equations. Reformulation Nonsmooth Piecewise Smooth Semismooth Smoothing Methods 355–369 (1999)

  30. Zhang, L., Zhou, W.: Spectral gradient projection method for solving nonlinear monotone equations. J. Comput. Appl. Math. 196(2), 478–484 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  31. Peiting, G., Chuanjiang, H.: A derivative-free three-term projection algorithm involving spectral quotient for solving nonlinear monotone equations. Optimization 67(10), 1631–1648 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  32. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. Ser. 91, 201–213 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  33. Figueiredo, M.A.T., Nowak, R.D., Wright, S.J.: Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007)

    Article  Google Scholar 

  34. Xiao, Y., Wang, Q., Hu, Q.: Non-smooth equations based method for \(\ell _1\)-norm problems with applications to compressed sensing. Nonlinear Anal. Theory Methods Appl. 74(11), 3570–3577 (2011)

    Article  MATH  Google Scholar 

  35. Waziri, M.Y., Ahmed, K., Halilu, A.S., Sabi’u, J.: Two new Hager-Zhang iterative schemes with improved parameter choices for monotone nonlinear systems and their applications in compressed sensing. RAIRO-Oper. Res. 56(1), 239–273 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  36. La Cruz, W., Martínez, J.M., Raydan, M.: Spectral residual method without gradient information for solving large-scale nonlinear systems: theory and experiments. Citeseer, Technical Report RT-04-08, 2004

  37. La Cruz, W.: A spectral algorithm for large-scale systems of nonlinear monotone equations. Numer. Algorithm. 76(4), 1109–1130 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhou, W., Shen, D.: An inexact PRP conjugate gradient method for symmetric nonlinear equations. Numer. Funct. Anal. Optim. 35(3), 370–388 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  39. Yu, G., Niu, S., Ma, J.: Multivariate spectral gradient projection method for nonlinear monotone equations with convex constraints. J. Ind. Manag. Optim. 9(1), 117–129 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  40. Liu, J., Feng, Y.: A derivative-free iterative method for nonlinear monotone equations with convex constraints. Numer. Algorithm. 82(1), 245–262 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  41. Lukšan, L., Matonoha, C., Vlcek, J.: Problems for nonlinear least squares and nonlinear equations. Technical report, Technical Report (2018)

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Acknowledgements

The authors would like to the Department of Mathematics, Khon Kaen University for allowing us their facilities. Also, the authors would like to thank the reviewers and the editors for their valuable suggestions which improved the earlier version of this paper.

Funding

This research was supported by the Postdoctoral Researcher Fellowship Training Program from Khon Kaen University (Grant No. PD2566-10).

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The two authors contributed equally.

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Correspondence to Thongchai Botmart.

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Appendix

Appendix

1.1 List of test problems

We use the following system of nonlinear equation for the experiments in Section 3 where \(Q(a)=(q_1(a),q_2(a),\ldots ,q_n(a))^T\) and \(a=(a_1, a_2,\ldots , a_n)^T.\)

Problem 5.1

[36]

$$\begin{aligned} q_1(a)= & {} e^{a_1}-1\\ q_i(a)= & {} e^{a_i}+a_{i-1}-1,\quad {}i=2,\ldots ,n. \end{aligned}$$

Problem 5.2

Logarithmic function [36]

$$\begin{aligned} q_i(a_i)=\log (a_i+1)-\frac{a_i}{n},\quad {}i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.3

[37]

$$\begin{aligned} q_i(a) = 2a_i - \sin |a_i|,\quad {}i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.4

[38]

$$\begin{aligned} q_i(a)=e^{a_i}-1, ~~ i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.5

[39]

$$\begin{aligned} q_i(a)=a_i-\sin (\vert a_i-1\vert ), ~~ i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.6

$$\begin{aligned} q_1(a)= & {} 2a_1-a_2+e^{a_1}-1,\\ q_i(a)= & {} -a_{i-1}+2a_i-a_{i+1}+e^{a_i}-1, ~~ i=2,\ldots ,n-1,\\ q_n(a)= & {} -a_{n-1}+2a_n+e^{a_n}-1. \end{aligned}$$

Problem 5.7

[40]

$$\begin{aligned} q_1(a)= & {} \frac{5}{2}a_1+a_2-1,\\ q_i(a)= & {} a_{i-1}+\frac{5}{2}a_i+a_{i+1}-1, ~~ i=2,\ldots ,n-1,\\ q_n(a)= & {} a_{n-1}+\frac{5}{2}a_n-1. \end{aligned}$$

Problem 5.8

$$\begin{aligned} q_i(a)=\frac{i}{n}e^{a_i}-1, ~~ i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.9

Tridiagonal exponential problem [41]

$$\begin{aligned} q_1(a)= & {} a_1-\exp \left( \cos \left( \frac{a_1+a_2}{n+1}\right) \right) \\ q_i(a)= & {} a_i-\exp \left( \cos \left( \frac{a_{i-1}+a_i+a_{i+1}}{n+1}\right) \right) ,~~2\le i \le n-1,\\ q_n(a)= & {} a_n-\exp \left( \cos \left( \frac{a_{n-1}+a_n}{n+1}\right) \right) . \end{aligned}$$

Problem 5.10

$$\begin{aligned} q_1(a)= & {} a_1 + \sin (a_1) - 1\\ q_i(a)= & {} -a_{i-1}+2a_i + \sin (a_i)-1, ~i=2,\ldots ,n-1,\\ q_n(a)= & {} a_n + \sin (a_n) - 1. \end{aligned}$$

Problem 5.11

$$\begin{aligned} q_i(a)=\frac{i}{n+1}e^{a_i}-1, ~~ i=1,2,\ldots ,n. \end{aligned}$$

Problem 5.12

$$\begin{aligned} q_i(a)= & {} a_i-\frac{1}{100}a_{i+1}^3, ~~ i=1,2,\ldots ,n-1,\\ q_n(a)= & {} a_n-\frac{1}{100}a_{1}^3. \end{aligned}$$

Problem 5.13

Problem 76 in [41]

$$\begin{aligned} q_i(a)= & {} a_i-\frac{1}{10}a_{i+1}^2,\quad i=1,2,\ldots ,n-1,\\ q_n(a)= & {} a_n-\frac{1}{10}a_{1}^2. \end{aligned}$$

Problem 5.14

Nonsmooth IVP problem

$$\begin{aligned} Q(a)=\left( \begin{array}{ccc} 4 &{} -1 &{} \\ -1 &{} 4 &{} -1 \\ \ddots &{} \ddots &{} \ddots \\ \ddots &{} \ddots &{} -1\\ &{} -1 &{} 4 \\ \end{array}\right) a+(e^{ a_1}-1,\ldots ,e^{ a_n}-1)^T. \end{aligned}$$

Problem 5.15

$$\begin{aligned} Q(a)=\left( \begin{array}{ccc} \frac{5}{2} &{} -1 &{} \\ -1 &{} \frac{5}{2} &{} -1 \\ \ddots &{} \ddots &{} \ddots \\ \ddots &{} \ddots &{} -1\\ &{} -1 &{} \frac{5}{2} \\ \end{array}\right) a+(1,1,\ldots ,1)^T. \end{aligned}$$

Problem 5.16

$$\begin{aligned} Q(a)=\left( \begin{array}{ccc} 2 &{} -1 &{} \\ 0 &{} 2 &{} -1 \\ \ddots &{} \ddots &{} \ddots \\ \ddots &{} \ddots &{} -1\\ {} &{} -1 &{} 2 \\ \end{array}\right) a+(\sin a_{1}-1,...,\sin a_{n}-1)^T. \end{aligned}$$

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Awwal, A.M., Botmart, T. A new sufficiently descent algorithm for pseudomonotone nonlinear operator equations and signal reconstruction. Numer Algor 94, 1125–1158 (2023). https://doi.org/10.1007/s11075-023-01530-z

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