Abstract
Gradients of objective functions in very many variables can be evaluated cheaply by the reverse, or adjoint, mode of automatic differentiation. One can simultaneously evaluate arbitrary Hessian-vector products with only a constant increase in complexity. These limited second derivatives can be used within a truncated Newton code or to improve nonlinear conjugate gradient codes with respect to the aspects: stepsize prediction, search direction conjugacy, restart criteria. We report preliminary results with an experimental conjugate gradient implementation.
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© 1995 Springer Science+Business Media New York
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Griewank, A., Fruth, M. (1995). Generating Conjugate Directions Using Limited Second Derivatives. In: Borggaard, J., Burkardt, J., Gunzburger, M., Peterson, J. (eds) Optimal Design and Control. Progress in Systems and Control Theory, vol 19. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-0839-6_11
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DOI: https://doi.org/10.1007/978-1-4612-0839-6_11
Publisher Name: Birkhäuser, Boston, MA
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