BibTeX
@ARTICLE{
         Conforti2001ACS,
       author = "D. Conforti and Marco Mancini",
       title = "A Curvilinear Search Algorithm for Unconstrained Optimization by Automatic
         Differentiation",
       journal = "Optimization Methods \& Software",
       volume = "15r",
       number = "3-4",
       pages = "283--297",
       doi = "10.1080/10556780108805822",
       key = "Conforti2001ACS",
       referred = "[Christianson2001GoP].",
       year = "2001",
       abstract = "Solving in an efficient and robust way an unconstrained optimization problem may
         prove quite hard in certain difficult situations. Typical examples are highly nonlinear problems,
         ill-conditioned and badly scaled problems. Particularly in these situations, it may be useful to
         compute a curvilinear trajectory and follow it by curvilinear searches with the aim to reach the
         solution in few long steps. In this paper, we proposed an approach for computing a suitable
         curvilinear trajectory, based on the knowledge of the third order derivatives of the objective
         function. The numerical implementation of this approach was made possible by Automatic
         Differentiation techniques. Some preliminary numerical results are very encouraging, especially in
         the case of very ill-conditioned and badly scaled problems.",
       ad_theotech = "Higher Order"
}