A Spectral Conjugate Gradient Method for Unconstrained Optimization
- Cite this article as:
- Birgin, E. & Martínez, J. Appl Math Optim (2001) 43: 117. doi:10.1007/s00245-001-0003-0
- 193 Downloads
A family of scaled conjugate gradient algorithms for large-scale unconstrained minimization is defined. The Perry, the Polak—Ribière and the Fletcher—Reeves formulae are compared using a spectral scaling derived from Raydan's spectral gradient optimization method. The best combination of formula, scaling and initial choice of step-length is compared against well known algorithms using a classical set of problems. An additional comparison involving an ill-conditioned estimation problem in Optics is presented.