This work is licensed under the Creative Commons Attribution 4.0 International License.
Brent R.P., Algorithms for Minimization Without Derivatives, Courier Corporation, Dover Publication, New York, USA, 2013.BrentR.P.Algorithms for Minimization Without DerivativesCourier Corporation, Dover PublicationNew York, USA2013Search in Google Scholar
Wu X., Shao H., Liu P., Zhang Y., Zhuo Y., An efficient conjugate gradient-based algorithm for unconstrained optimization and its projection extension to large-scale constrained nonlinear equations with applications in signal recovery and image denoising problems, Journal of Computational and Applied Mathematics, 422, 114879, 2023.WuX.ShaoH.LiuP.ZhangY.ZhuoY.An efficient conjugate gradient-based algorithm for unconstrained optimization and its projection extension to large-scale constrained nonlinear equations with applications in signal recovery and image denoising problemsJournal of Computational and Applied Mathematics4221148792023Search in Google Scholar
Davidon W.C., Variable metric method for minimization, Argonne National Laboratory, Lemont, Illinois, (Master Thesis), ANL-5990, University of Chicago, USA, 1959.DavidonW.C.Variable metric method for minimization, Argonne National Laboratory, Lemont, Illinois(Master Thesis), ANL-5990,University of ChicagoUSA1959Search in Google Scholar
Fletcher R., Powell M.J.D., A rapidly convergent descent method for minimization, The Computer Journal, 6(2), 163–168, 1963.FletcherR.PowellM.J.D.A rapidly convergent descent method for minimizationThe Computer Journal621631681963Search in Google Scholar
Hestenes M.R., Stiefel E., Methods of conjugate gradients for solving linear systems, Journal of Research of the National Bureau of Standards, 49(6), 409–436, 1952.HestenesM.R.StiefelE.Methods of conjugate gradients for solving linear systemsJournal of Research of the National Bureau of Standards4964094361952Search in Google Scholar
Fletcher R., Reeves C.M., Function minimization by conjugate gradients, The Computer Journal, 7(2), 149–154, 1964.FletcherR.ReevesC.M.Function minimization by conjugate gradientsThe Computer Journal721491541964Search in Google Scholar
Nocedal J., Wright S.J., Numerical Optimization (2nd Ed.), Springer, USA, 2006.NocedalJ.WrightS.J.Numerical Optimization2nd Ed.SpringerUSA2006Search in Google Scholar
Yousif O.O.O., Mohammed M.A.Y., Saleh M.A., Elbashir M.K., A criterion for the global convergence of conjugate gradient methods under strong Wolfe line search, Journal of King Saud University-Science, 34(8), 102281, 2022.YousifO.O.O.MohammedM.A.Y.SalehM.A.ElbashirM.K.A criterion for the global convergence of conjugate gradient methods under strong Wolfe line searchJournal of King Saud University-Science3481022812022Search in Google Scholar
Kelley C.T., Iterative Methods for Optimization, SIAM, Philadelphia, USA, 1999.KelleyC.T.Iterative Methods for OptimizationSIAMPhiladelphia, USA1999Search in Google Scholar
Zhang L., An improved Wei-Yao-Liu nonlinear conjugate gradient method for optimization computation, Applied Mathematics and Computation, 215(6), 2269–2274, 2009.ZhangL.An improved Wei-Yao-Liu nonlinear conjugate gradient method for optimization computationApplied Mathematics and Computation2156226922742009Search in Google Scholar
Doikov N., Nesterov Y., Gradient regularization of Newton method with Bregman distances, Mathematical Programming, DOI:10.1007/s10107-023-01943-7, 2023.DoikovN.NesterovY.Gradient regularization of Newton method with Bregman distancesMathematical Programming10.1007/s10107-023-01943-72023Open DOISearch in Google Scholar
Stanimirović P.S., Shaini B.I., Sabi’u J., Shah A., Petrović M.J., Ivanov B., Cao X., Stupina A., Li S., Improved gradient descent iterations for solving systems of nonlinear equations, Algorithms, 16(64), 1–23, 2023.StanimirovićP.S.ShainiB.I.Sabi’uJ.ShahA.PetrovićM.J.IvanovB.CaoX.StupinaA.LiS.Improved gradient descent iterations for solving systems of nonlinear equationsAlgorithms16641232023Search in Google Scholar
Gupta S.D., Freund R.M., Sun X.A., Taylor A., Nonlinear conjugate gradient methods: worst-case convergence rates via computer-assisted analyses, arXiv:2301.01530, 2023.GuptaS.D.FreundR.M.SunX.A.TaylorA.Nonlinear conjugate gradient methods: worst-case convergence rates via computer-assisted analysesarXiv:2301.01530,2023Search in Google Scholar
Ortega J.M., Rheinboldt W.C., Iterative solution of nonlinear equations in several variables, SIAM, Philadelphia, USA, 1999.OrtegaJ.M.RheinboldtW.C.Iterative solution of nonlinear equations in several variablesSIAMPhiladelphia, USA1999Search in Google Scholar
Hestenes M.R., Conjugate Direction Methods in Optimization (Vol:12), Springer, New York, USA, 1980.HestenesM.R.Conjugate Direction Methods in Optimization12SpringerNew York, USA1980Search in Google Scholar
Russak I.B., Convergence of the conjugate Gram-Schmidt method, Journal of Optimization Theory and Applications, 33, 163–173, 1981.RussakI.B.Convergence of the conjugate Gram-Schmidt methodJournal of Optimization Theory and Applications331631731981Search in Google Scholar
Stein J.I., Conjugate Direction Algorithms in Numerical Analysis and Optimization, (Final Report), U.S. Army Research Office, DAHC 04-74-G-0006, National Science Foundation GP-40175, The University of Toledo, Toledo, Ohio, USA, 1975.SteinJ.I.Conjugate Direction Algorithms in Numerical Analysis and Optimization(Final Report), U.S. Army Research Office, DAHC 04-74-G-0006, National Science Foundation GP-40175The University of Toledo, Toledo, Ohio, USA1975Search in Google Scholar
Stein J.I., Raihen M.N., Convergence rates for Hestenes’ Gram-Schmidt conjugate direction method without derivatives in numerical optimization, AppliedMath, 3(2), 268–285, 2023.SteinJ.I.RaihenM.N.Convergence rates for Hestenes’ Gram-Schmidt conjugate direction method without derivatives in numerical optimizationAppliedMath322682852023Search in Google Scholar
Dennemeyer R.F., Mookini E.H., CGS algorithms for unconstrained minimization of functions, Journal of Optimization Theory and Applications, 16(1–2), 67–85, 1975.DennemeyerR.F.MookiniE.H.CGS algorithms for unconstrained minimization of functionsJournal of Optimization Theory and Applications161–267851975Search in Google Scholar
Ortega J.M., Rheinboldt W.C., On a class of approximate iterative processes, Archive for Rational Mechanics and Analysis, 23, 352–365, 1967.OrtegaJ.M.RheinboldtW.C.On a class of approximate iterative processesArchive for Rational Mechanics and Analysis233523651967Search in Google Scholar
Raihen N., Convergence Rates for Hestenes’ Gram-Schmidt Conjugate Direction Method without Derivatives in Numerical Optimization, (Masters Thesis), University of Toledo, Toledo, USA, 2017.RaihenN.Convergence Rates for Hestenes’ Gram-Schmidt Conjugate Direction Method without Derivatives in Numerical Optimization(Masters Thesis),University of ToledoToledo, USA2017Search in Google Scholar
Andrei N., Nonlinear Conjugate Gradient Methods for Unconstrained Optimization, Springer, Switzerland, 2020.AndreiN.Nonlinear Conjugate Gradient Methods for Unconstrained OptimizationSpringerSwitzerland2020Search in Google Scholar
Olsen N.C., Private Communication to Ivie Stein Jr., Consultant, Lockheed, Palmdale, California, USA, 2005.OlsenN.C.Private Communication to Ivie Stein Jr., ConsultantLockheedPalmdale, California, USA2005Search in Google Scholar
Desrochers A., Mohseni S., Quadratic optimization via conjugate directions and projection matrices, 1985 American Control Conference, IEEE, 19–21 June 1985, Boston, Massachusetts, USA, 1684–1688, 1985.DesrochersA.MohseniS.Quadratic optimization via conjugate directions and projection matrices1985 American Control Conference, IEEE19–21 June 1985Boston, Massachusetts, USA168416881985Search in Google Scholar
Stein I., Conjugate gradient methods in Banach spaces, Nonlinear Analysis: Theory Methods and Applications, 63(5–7), e2621–e2628, 2005.SteinI.Conjugate gradient methods in Banach spacesNonlinear Analysis: Theory Methods and Applications635–7e2621e26282005Search in Google Scholar