CasADi: a software framework for nonlinear optimization and optimal control

By Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, Moritz Diehl. 2018

4,055 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Applied Mathematics, Control and Systems Engineering, Mathematical Optimization, Numerical Methods & Scientific Computing

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Citations in the corpus

Neural Ordinary Differential Equations (2018, neutral): “The Stan library (Carpenter et al., 2015) implements gradient estimation through ODE solutions using forward sensitivity analysis. However, forward sensitivity analysis is quadratic-time in the number of variables, whereas the adjoint sensitivity analysis is linear (Carpenter et al., 2015; Zhang and”
Enabling New Flexibility in the SUNDIALS Suite of Nonlinear and Differential/Algebraic Equation Solvers (2020, background): “2020] as well as in other mathematical libraries and frameworks [Anderson et al. 2020; Andersson et al. 2019; Arndt et al. 2020; Malengier et al. 2018; Rackauckas and Nie 2017; Schmidt 2007; Wolfram 2020; Zhang et al. 2020].”
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning (2021, methodology): “(18, 5) is the extension of the traditional API (145) with features to facilitate: (i) the evaluation of safe learning control approaches (such as the randomization of inertial properties and of initial positions and velocities, random and adversarial disturbances, and the speciﬁcation and evaluatio”
Model Predictive Control of Power Electronic Systems: Methods, Results, and Challenges (2020, background): “, ACADO [188], casADi [189], VIATOC [206], etc.”
Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control (2022, methodology): “We construct an expert control policy by solving a nonlinear MPC problem using CasADi [69].”
Scalable Gradients for Stochastic Differential Equations (2020, methodology): “This method was well-known in the physics, numerical analysis, and control communities for decades [3, 4, 57, 62].”
Data-Driven MPC for Quadrotors (2021, methodology): “[34] J. A. E. Andersson, J. Gillis, G. Horn, J. B. Rawlings, and M. Diehl, “CasADi -”
Efficient and Modular Implicit Differentiation (2021, background): “CasADi [7] allows to differentiate various optimization and root ﬁnding problem algorithms provided by the library.”
Mini Cheetah: A Platform for Pushing the Limits of Dynamic Quadruped Control (2019, methodology): “1) Backflip Trajectory Optimization: To generate a backflip trajectory, we used the open-source CasADi library [18] to set up a trajectory optimization problem to be solved by IPOPT [19].”