URL = {https://doi.org/10.1137/0917003}
}
+@book{DennisSchnabel96,
+ author = {J. E. Dennis and R. B. Schnabel},
+ title = {Numerical Methods for Unconstrained Optimization and Nonlinear Equations},
+ publisher = {Society for Industrial and Applied Mathematics},
+ year = {1996},
+ doi = {10.1137/1.9781611971200},
+ URL = {https://epubs.siam.org/doi/abs/10.1137/1.9781611971200}
+}
+
%-------------------------------------------------------------------------------
% Step 78
* way for furthering convergence of the nonlinear problem. This technique is
* implemented in the second strategy, called Linesearch. This option employs
* both the $\alpha$ and $\beta$ conditions of the Goldstein-Armijo
- * linesearch algorithm given in *J. E. Dennis and R. B. Schnabel. "Numerical
- * Methods for Unconstrained Optimization and Nonlinear Equations." SIAM,
- * Philadelphia, 1996.*, where $\lambda$ is chosen to guarantee a sufficient
+ * linesearch algorithm given in @cite DennisSchnabel96 ,
+ * where $\lambda$ is chosen to guarantee a sufficient
* decrease in $F$ relative to the step length as well as a minimum step
* length relative to the initial rate of decrease of $F$. One property of the
* algorithm is that the full Newton step tends to be taken close to the
* produce the new iterate. Next, the nonlinear residual function is
* evaluated at the new iterate, and convergence is checked. The Picard and
* fixed point methods can be significantly accelerated using Anderson's
- * method.
+ * acceleration method.
*
- * The user has to provide the implementation of the following std::functions:
+ * The user has to provide the implementation of the following
+ * `std::function`s:
* - reinit_vector;
* and only one of
* - residual;
* scaling factors for both the solution and the residual evaluation during
* convergence checks:
* - get_solution_scaling;
- * - get_function_scaling;
+ * - get_function_scaling.
*/
template <typename VectorType = Vector<double>>
class KINSOL