Free Access
Issue
RAIRO-Oper. Res.
Volume 55, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
Page(s) S1113 - S1127
DOI https://doi.org/10.1051/ro/2020061
Published online 02 March 2021
  • N. Andrei, Open problems in nonlinear conjugate gradient algorithms for unconstrained optimization. Bull. Malays. Math. Sci. Soc. 34 (2011) 319–330. [Google Scholar]
  • S. Babaie-Kafaki and R. Ghanbari, The Dai–Liao nonlinear conjugate gradient method with optimal parameter choices. Eur. J. Oper. Res. 234 (2014) 625–630. [Google Scholar]
  • J. Barzilai and J.M. Borwein, Two point stepsize gradient methods. IMA J. Numer. Anal. 8 (1988) 141–148. [Google Scholar]
  • W. Cheng, A PRP type method for systems of monotone equations. Math. Comput. Model. 50 (2009) 15–20. [Google Scholar]
  • Y.H. Dai and L.Z. Liao, New conjugacy conditions and related nonlinear conjugate gradient methods. Appl. Math. Optim. 43 (2001) 87–101. [Google Scholar]
  • Y.H. Dai and Y. Yuan, A nonlinear conjugate gradient method with a strong global convergence property. SIAM J. Optim. 10 (1999) 177–182. [Google Scholar]
  • Z. Dai and H. Zhu, A modified Hestenes–Stiefel-type derivative-free method for large-scale nonlinear monotone equations. Mathematics 8 (2020) 168. [Google Scholar]
  • Z.F. Dai, X. Chen and F. Wen, A modifed Perry’s conjugate gradient method-based derivative-free method for solving largescale nonlinear monotone equations. Appl. Math. Comput. 270 (2015) 378–386. [Google Scholar]
  • Z. Dai, H. Zhou, F. Wen and S. He, Efficient predictability of stock return volatility: the role of stock market implied volatility. North Am. J. Econ. Finance 52 (2020) 101174. [Google Scholar]
  • J.E. Dennis and R.B. Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, Englewood Cliffs, NJ (1983). [Google Scholar]
  • E.D. Dolan and J.J. Moré, Benchmarking optimization software with performance profiles. Math. Program. 91 (2002) 201–213. [Google Scholar]
  • M. Figueiredo, R. Nowak and S.J. Wright, Gradient projection for sparse reconstruction, application to compressed sensing and other inverse problems. IEEE J. Sel. Top. Sign. Proces. 1 (2007) 586–597. [Google Scholar]
  • P. Gao and C. He, An efficient three-term conjugate gradient method for nonlinear monotone equations with convex constraints. Calcolo 55 (2018) 53. [CrossRef] [Google Scholar]
  • W.W. Hager and H. Zhang, A survey of nonlinear conjugate gradient methods. Pac. J. Optim. 2 (2006) 35–58. [Google Scholar]
  • M.R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems. J. Res. Natl. Bur. Stand. 49 (1952) 409–436. [Google Scholar]
  • Y. Hu and Z. Wei, A modified Liu–Storey conjugate gradient projection algorithm for nonlinear monotone equations. Int. Math. Forum 9 (2014) 1767–1777. [Google Scholar]
  • A.N. Iusem and M.V. Solodov, Newton-type methods with generalized distances for constrained optimization. Optimization 41 (1997) 257–278. [Google Scholar]
  • H. Kobayashi, Y. Narushima and H. Yabe, Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization. Optim. Methods Softw. 32 (2017) 1313–1329. [Google Scholar]
  • M. Koorapetse and P. Kaelo, Self adaptive spectral conjugate gradient method for solving nonlinear monotone equations. J. Egypt. Math. Soc. 28 (2020) 4. [Google Scholar]
  • Q. Li and D.H. Li, A class of derivative-free methods for large-scale nonlinear monotone equations. IMA J. Numer. Anal. 31 (2011) 1625–1635. [Google Scholar]
  • J. Liu and S. Li, Spectral DY-type projection method for nonlinear monotone systems of equations. J. Comput. Math. 33 (2015) 341–354. [Google Scholar]
  • K. Meintjes and A.P. Morgan, A methodology for solving chemical equilibrium systems. Appl. Math. Comput. 22 (1987) 333–361. [Google Scholar]
  • Y. Narushima and H. Yabe, A survey of sufficient descent conjugate gradient methods for unconstrained optimization. SUT J. Math. 50 (2014) 167–203. [Google Scholar]
  • Y. Narushima, H. Yabe and J.A. Ford, A three-term conjugate gradient method with sufficient descent property for unconstrained optimization. SIAM J. Optim. 21 (2011) 212–230. [Google Scholar]
  • J.M. Ortega and W.C. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables. SIAM, Philadelphia, PA (1970). [Google Scholar]
  • J. Sabi’u, A. Shah and M.Y. Waziri, Two optimal Hager–Zhang conjugate gradient methods for solving monotone nonlinear equations. Appl. Numer. Math. 153 (2020) 217–233. [Google Scholar]
  • M.V. Solodov and B.F. Svaiter, A globally convergent inexact Newton method for systems of monotone equations. Reformulation: Nonsmooth, Piecewise Smooth, Semismooth and Smoothing Methods, edited by M. Fukushima and L. Qi. In Vol. 22 of Applied Optimization. Springer (1998) 355–369. [Google Scholar]
  • S. Wang and H. Guan, A scaled conjugate gradient method for solving monotone nonlinear equations with convex constraints. J. Appl. Math. 2013 (2013) 286486. [Google Scholar]
  • M.Y. Waziri, K.A. Hungu and J. Sabi’u, Descent Perry conjugate gradient methods for systems of monotone nonlinear equations. Numer. Algorithms 85 (2020) 763–785. [Google Scholar]
  • L. Zhang and W. Zhou, Spectral gradient projection method for solving nonlinear monotone equations. J. Comput. Appl. Math. 196 (2006) 478–484. [Google Scholar]
  • Y.B. Zhao and D.H. Li, Monotonicity of fixed point and normal mapping associated with variational inequality and its application. SIAM J. Optim. 4 (2001) 962–973. [Google Scholar]
  • W.J. Zhou and D.H. Li, Limited memory BFGS method for nonlinear monotone equations. J. Comput. Math. 25 (2007) 89–96. [Google Scholar]
  • W.J. Zhou and D.H. Li, A globally convergent BFGS method for nonlinear monotone equations without any merit functions. Math. Comput. 77 (2008) 2231–2240. [Google Scholar]
  • G. Zhou and K.C. Toh, Superlinear convergence of a Newton-type algorithm for monotone equations. J. Optim. Theory App. 125 (2005) 205–221. [Google Scholar]

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