[1] A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and Z. Kolter. Differentiable convex optimization layers. In Advances in Neural Information Processing Systems, 2019.
[2] B. Amos, I. Jimenez, J. Sacks, B. Boots, and J. Z. Kolter. Differentiable MPC for End-to-end Planning and Control. Advances in Neural Information Processing Systems, 2018.
[3] T. Bāsār and G. J. Olsder. Dynamic noncooperative game theory. Academic Press, London/New York, 1982. ISBN 978-0-08-095666-4.
[4] D. Bertsekas. Reinforcement learning and optimal control. Athena Scientific, 2019.
[5] R. Bevilacqua, B. Codenotti, and F. Romani. Parallel solution of block tridiagonal linear systems. Linear Algebra and its Applications, 104:39-57, 1988.
[6] M. Bhardwaj, B. Boots, and M. Mukadam. Differentiable Gaussian process motion planning. Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2020.
[7] M. Blondel, O. Teboul, Q. Berthet, and J. Djolonga. Fast differentiable sorting and ranking. In Proc. of the International Conference on Machine Learning (ICML), 2020.
[8] S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004. doi: 10.1017/CBO9780511804441.
[9] D. Campbell, L. Liu, and S. Gould. Solving the Blind Perspective-n-Point Problem End-To-End With Robust Differentiable Geometric Optimization. In Proc. of the European Conference on Computer Vision (ECCV), 2020.
[10] K. Cao and L. Xie. Game-Theoretic Inverse Reinforcement Learning: A Differential Pontryagin's Maximum Principle Approach. IEEE Transactions on Neural Networks and Learning Systems, pages 1-8, 2022. doi: 10.1109/TNNLS.2022.3148376.
[11] A. Cherian, B. Fernando, M. Harandi, and S. Gould. Generalized rank pooling for activity recognition. In Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
[12] P. Englert, N. A. Vien, and M. Toussaint. Inverse KKT: Learning cost functions of manipulation tasks from demonstrations. The International Journal of Robotics Research, 36(13-14):1474-1488, 2017. doi: 10.1177/0278364917745980.
[13] P. E. Gill, W. Murray, and M. A. Saunders. SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization. SIAM Review, page 99-131, 2005.
[14] S. Gould, R. Hartley, and D. Campbell. Deep Declarative Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44:3988-4004, Aug 2022. doi: 10.1109/TPAMI.2021.3059462.
[15] I. N. Hajj and S. Skelboe. A multilevel parallel solver for block tridiagonal and banded linear systems. Parallel Computing, 15:21-45, 1990.
[16] C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant. Array programming with NumPy. Nature, 585:357-362, 2020.
[17] K. Hatz, J. P. Schlöder, and H. G. Bock. Estimating parameters in optimal control problems. SIAM Journal on Scientific Computing, 34(3):A1707-A1728, 2012. doi: 10.1137/110823390.
[18] S. Hirshman, K. Perumalla, V. Lynch, and R. Sanchez. Bcyclic: A parallel block tridiagonal matrix cyclic solver. Journal of Computational Physics, 229:6392-6404, 2010.
[19] T. A. Howell, B. E. Jackson, and Z. Manchester. ALTRO: A Fast Solver for Constrained Trajectory Optimization. In Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7674-7679, 2019.