![]() ![]() Finally, we compare the AD-assisted approaches to a We also observe that AD technologyĬan increase the efficiency of the standard quasi-Newton (positive definite secant) approach to the full nonlinear minimizationĪpproach to this problem and we compare these two AD-assisted methods. Made practical due to the selective application of automatic differentiation (AD) technology. In this paper we propose and investigate an efficient quasi-Newton (secant) approach to the nonlinear least-squares problem, Quasi-Newton contribution to practical nonlinear optimization is unchallenged. In addition, most modern optimization libraries house a quasi-Newton collection of codes and they are widely used. Optimization and Nonlinear Equations, 1996). Properties of these methods and illustrating their performance (e.g., Dennis and Schnabel, Numerical Methods for Unconstrained Solution of nonlinear minimization problems and in multi-dimensional zero-finding. Quasi-Newton methods have played a prominent role, over many years, in the design of effective practical methods for the numerical ![]()
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