Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement.

ABSTRACT: Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive sim

SUBMITTER: Falisse A 

PROVIDER: S-EPMC6797126 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature