On the convergence rate of Fletcher?Reeves nonlinear conjugate gradient methods satisfying strong Wolfe conditions: Application to parameter identification in problems governed by general dynamics

Over the last decades, many efforts have been made toward understanding of convergence rate gradient-based method for both constrained and unconstrained optimization. The cases strongly convex weakly payoff function extensively studied are nowadays fully understood. Despite impressive advances in optimization context, nonlinear non-convex problems still not exploited. In this paper, we concerned with nonlinear, problem under system dynamic constraints. We apply our analysis to parameter identification systems governed by general differential equations. considered inverse is presented using optimal control tools. tackle through use Fletcher-Reeves conjugate gradient satisfying strong Wolfe conditions inexact line search. rigorously establish a report new linear which forms main contribution work. theoretical result reported requires that second derivative functional be continuous bounded. Numerical evidence on selection popular models as direct application support findings.