• 1.

    Xia, Z., Wang, X., Sun, X., Wang, Q.: A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 27(2), 340–352 (2015)


    Google Scholar
     

  • 2.

    Yuan, G., Lu, S., Wei, Z.: A new trust-region method with line search for solving symmetric nonlinear equations. Int. J. Comput. Math. 88(10), 2109–2123 (2011)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 3.

    Sulaiman, I.M., Supian, S., Mamat, M.: New Class of Hybrid Conjugate Gradient Coefficients with Guaranteed Descent and Efficient Line Search. In IOP Conference Series: Materials Science and Engineering, vol. 621, p. 012021. IOP Publishing, Bristol (2019)


    Google Scholar
     

  • 4.

    Hager, W.W., Zhang, H.: A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2(1), 35–58 (2006)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 5.

    Stiefel, E.: Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49, 409–435 (1952)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 6.

    Fletcher, R., Powell, M.J.D.: A rapidly convergent descent method for minimization. Comput. J. 6(2), 163–168 (1963)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 7.

    Polak, E., Ribiere, G.: Note sur la convergence de méthodes de directions conjuguées. ESAIM: Math. Model. Numer. Anal. 3(R1), 35–43 (1969)

    MATH 

    Google Scholar
     

  • 8.

    Polyak, B.T.: The conjugate gradient method in extremal problems. USSR Comput. Math. Math. Phys. 9(4), 94–112 (1969)

    MATH 

    Google Scholar
     

  • 9.

    Liu, Y., Storey, C.: Efficient generalized conjugate gradient algorithms, part 1: theory. J. Optim. Theory Appl. 69(1), 129–137 (1991)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 10.

    Dai, Y., Han, J., Liu, G., Sun, D., Yin, H., Yuan, Y.X.: Convergence properties of nonlinear conjugate gradient methods. SIAM J. Optim. 10(2), 345–358 (2000)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 11.

    Yuan, G., Wei, Z., Lu, X.: Global convergence of BFGS and PRP methods under a modified weak Wolfe–Powell line search. Appl. Math. Model. 47, 811–825 (2017)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 12.

    Rivaie, M., Mamat, M., June, L.W., Mohd, I.: A new class of nonlinear conjugate gradient coefficients with global convergence properties. Appl. Math. Comput. 218(22), 11323–11332 (2012)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 13.

    Dai, Z.: Comments on a new class of nonlinear conjugate gradient coefficients with global convergence properties. Appl. Math. Comput. 276, 297–300 (2016)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 14.

    Yousif, O.O.O.: The convergence properties of RMIL+ conjugate gradient method under the strong Wolfe line search. Appl. Math. Comput. 367, 124777 (2020)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 15.

    Al-Baali, M.: Descent property and global convergence of the Fletcher–Reeves method with inexact line search. IMA J. Numer. Anal. 5(1), 121–124 (1985)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 16.

    Gilbert, J.C., Nocedal, J.: Global convergence properties of conjugate gradient methods for optimization. SIAM J. Optim. 2(1), 21–42 (1992)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 17.

    Touati-Ahmed, D., Storey, C.: Efficient hybrid conjugate gradient techniques. J. Optim. Theory Appl. 64(2), 379–397 (1990)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 18.

    Hu, Y.F., Storey, C.: Global convergence result for conjugate gradient methods. J. Optim. Theory Appl. 71(2), 399–405 (1991)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 19.

    Awwal, A.M., Sulaiman, I.M., Malik, M., Mamat, M., Kumam, P., Sitthithakerngkiet, K.: A spectral RMIL+ conjugate gradient method for unconstrained optimization with applications in portfolio selection and motion control. IEEE Access 9, 75398–75414 (2021)


    Google Scholar
     

  • 20.

    Beale, E.M.L.: A deviation of conjugate gradients. In: Numerical Methods for Nonlinear Optimization, pp. 39–43 (1972)


    Google Scholar
     

  • 21.

    McGuire, M.F., Wolfe, P.: Evaluating a restart procedure for conjugate gradients. IBM Thomas J. Watson Research Division (1973)

  • 22.

    Zhang, L., Zhou, W., Li, D.H.: A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence. IMA J. Numer. Anal. 26(4), 629–640 (2006)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 23.

    Liu, J.K., Feng, Y.M., Zou, L.M.: Some three-term conjugate gradient methods with the inexact line search condition. Calcolo 55(2), 1–16 (2018)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 24.

    Zhang, L., Zhou, W., Li, D.: Some descent three-term conjugate gradient methods and their global convergence. Optim. Methods Softw. 22(4), 697–711 (2007)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 25.

    Andrei, N.: A simple three-term conjugate gradient algorithm for unconstrained optimization. J. Comput. Appl. Math. 241, 19–29 (2013)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 26.

    Al-Bayati, A.Y., Altae, H.W.: A new three-term non-linear conjugate gradient method for unconstrained optimization. Can. J. Sci. Eng. Math. Can. 1, 108–124 (2010)


    Google Scholar
     

  • 27.

    Dong, X., Liu, H., He, Y., Babaie-Kafaki, S., Ghanbari, R.: A new three–term conjugate gradient method with descent direction for unconstrained optimization. Math. Model. Anal. 21(3), 399–411 (2016)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 28.

    Sun, M., Liu, J.: Three modified Polak–Ribiere–Polyak conjugate gradient methods with sufficient descent property. J. Inequal. Appl. 2015(1), 1 (2015)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 29.

    Zoutendijk, G.: Nonlinear programming, computational methods. In: Integer and Nonlinear Programming, pp. 37–86 (1970)

    MATH 

    Google Scholar
     

  • 30.

    Andrei, N.: Nonlinear Conjugate Gradient Methods for Unconstrained Optimization. Springer, Berlin (2020)

    MATH 

    Google Scholar
     

  • 31.

    Jamil, M., Yang, X.S.: A literature survey of benchmark functions for global optimisation problems. Int. J. Math. Model. Numer. Optim. 4(2), 150–194 (2013)

    MATH 

    Google Scholar
     

  • 32.

    Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 33.

    World heath organization: Report on coronavirus (COVID-19) (2020)

  • 34.

    Sulaiman, I.M., Mamat, M.: A new conjugate gradient method with descent properties and its application to regression analysis. J. Numer. Anal. Ind. Appl. Math. 14(1–2), 25–39 (2020)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 35.

    ul Rehman, A., Singh, R., Agarwal, P.: Modeling, analysis and prediction of new variants of COVID-19 and Dengue co-infection on complex network. Chaos Solitons Fractals 2021, 111008 (2021)

    MathSciNet 

    Google Scholar
     

  • 36.

    Zhang, Y., He, L., Hu, C., Guo, J., Li, J., Shi, Y.: General four-step discrete-time zeroing and derivative dynamics applied to time-varying nonlinear optimization. J. Comput. Appl. Math. 347, 314–329 (2019)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 37.

    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)


    Google Scholar
     

  • 38.

    Yahaya, M.M., Kumam, P., Awwal, A.M., Aji, S.: A structured quasi–Newton algorithm with nonmonotone search strategy for structured NLS problems and its application in robotic motion control. J. Comput. Appl. Math. 395, 113582 (2021)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 39.

    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)

    MATH 

    Google Scholar
     

  • 40.

    Awwal, A.M., Kumam, P., Mohammad, H.: Iterative algorithm with structured diagonal Hessian approximation for solving nonlinear least squares problems. J. Nonlinear Convex Anal. 22(6), 1173–1188 (2021)

    MathSciNet 
    MATH 

    Google Scholar
     

  • 41.

    Agarwal, P., Ahsan, S., Akbar, M., Nawaz, R., Cesarano, C.: A reliable algorithm for solution of higher dimensional nonlinear ((1+ 1)) and ((2+ 1)) dimensional Volterra–Fredholm integral equations. Dolomites Res. Notes Approx. 14(2), 18–25 (2021)

    MathSciNet 

    Google Scholar
     

  • 42.

    Shah, N.A., Agarwal, P., Chung, J.D., El-Zahar, E.R., Hamed, Y.S.: Analysis of optical solitons for nonlinear Schrödinger equation with detuning term by iterative transform method. Symmetry 12(11), 1850 (2020)


    Google Scholar
     

  • 43.

    Saoudi, K., Agarwal, P., Mursaleen, M.: A multiplicity result for a singular problem with subcritical nonlinearities. J. Nonlinear Funct. Anal., 1–18 (2017)

  • 44.

    Rahmoune, A., Ouchenane, D., Boulaaras, S., Agarwal, P.: Growth of solutions for a coupled nonlinear Klein–Gordon system with strong damping, source, and distributed delay terms. Adv. Differ. Equ. 2020(1), 1 (2020)

    MathSciNet 

    Google Scholar
     

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