References
- J. Barzilai and J. M. Borwein, Two-point step size gradient methods, IMA J. Numer. Anal. 8 (1988), no. 1, 141-148 https://doi.org/10.1093/imanum/8.1.141
- E. G. Birgin and J. M. Martinez, A spectral conjugate gradient method for unconstrained optimization, Appl. Math. Optim. 43 (2001), no. 2, 117-128 https://doi.org/10.1007/s00245-001-0003-0
- E. G. Birgin, J. M. Martinez, and M. Raydan, Nonmonotone spectral projected gradient methods on convex sets, SIAM J. Optim. 10 (2000), no. 4, 1196-1211 https://doi.org/10.1137/S1052623497330963
- E. G. Birgin, J. M. Martinez, and M. Raydan, Spectral projected gradient methods, Encyclopedia of Optimization, C. A. Floudas and P. M. Pardalos, (Eds.), to appear
- E. E. Cragg and A. V. Levy, Study on a supermemory gradient method for the minimization of functions, J. Optimization Theory Appl. 4 (1969), 191-205 https://doi.org/10.1007/BF00930579
- L. Grippo, F. Lampariello, and S. Lucidi, A nonmonotone line search technique for Newton's method, SIAM J. Numer. Anal. 23 (1986), no. 4, 707-716 https://doi.org/10.1137/0723046
- W. Hock and K. Schittkowski, Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, 187. Springer-Verlag, Berlin-New York, 1981
- A. Miele and J. W. Cantrell, Study on a memory gradient method for the minimization of functions, J. Optimization Theory Appl. 3 (1969), 459-470 https://doi.org/10.1007/BF00929359
- Y. Narushima and H. Yabe, Global convergence of a memory gradient method for unconstrained optimization, Comput. Optim. Appl. 35 (2006), no. 3, 325-346 https://doi.org/10.1007/s10589-006-8719-z
- M. Raydan, The Barzilai and Borwein gradient method for the large scale unconstrained minimization problem, SIAM J. Optim. 7 (1997), no. 1, 26-33 https://doi.org/10.1137/S1052623494266365
- K. Schittkowski, More Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Mathematical Systems, 282. Springer-Verlag, Berlin, 1987
- P. L. Toint, Non-monotone trust-region algorithms for nonlinear optimization subject to convex constraints, Math. Programming 77 (1997), no. 1, Ser. A, 69-94 https://doi.org/10.1007/BF02614518