@article{GriewankHasenfelderRadonsetal.2018, author = {Griewank, Andreas and Hasenfelder, Richard and Radons, Manuel and Lehmann, Lutz and Streubel, Tom}, title = {Integrating Lipschitzian dynamical systems using piecewise algorithmic differentiation}, series = {Optimization Methods and Software}, volume = {33}, journal = {Optimization Methods and Software}, publisher = {Taylor \& Francis}, doi = {10.1080/10556788.2017.1378653}, pages = {1089 -- 1107}, year = {2018}, abstract = {In this article we analyse a generalized trapezoidal rule for initial value problems with piecewise smooth right-hand side F : IR^n -> IR^n based on a generalization of algorithmic differentiation. When applied to such a problem, the classical trapezoidal rule suffers from a loss of accuracy if the solution trajectory intersects a nondifferentiability of F. The advantage of the proposed generalized trapezoidal rule is threefold: Firstly, we can achieve a higher convergence order than with the classical method. Moreover, the method is energy preserving for piecewise linear Hamiltonian systems. Finally, in analogy to the classical case we derive a third-order interpolation polynomial for the numerical trajectory. In the smooth case, the generalized rule reduces to the classical one. Hence, it is a proper extension of the classical theory. An error estimator is given and numerical results are presented.}, language = {en} } @article{GriewankStreubelLehmannetal.2017, author = {Griewank, Andreas and Streubel, Tom and Lehmann, Lutz and Radons, Manuel and Hasenfelder, Richard}, title = {Piecewise linear secant approximation via algorithmic piecewise differentiation}, series = {Optimization Methods and Software}, volume = {33}, journal = {Optimization Methods and Software}, number = {4-6}, publisher = {Taylor \& Francis}, doi = {10.1080/10556788.2017.1387256}, pages = {1108 -- 1126}, year = {2017}, abstract = {It is shown how piecewise differentiable functions F : IR^n -> IR^m that are defined by evaluation programmes can be approximated locally by a piecewise linear model based on a pair of sample points \check x and \hat x. We show that the discrepancy between function and model at any point x is of the bilinear order O(||x - \check x||*||x - \hat x||). As an application of the piecewise linearization procedure we devise a generalized Newton's method based on successive piecewise linearization and prove for it sufficient conditions for convergence and convergence rates equalling those of semismooth Newton. We conclude with the derivation of formulas for the numerically stable implementation of the aforedeveloped piecewise linearization methods.}, language = {en} }