References
Bambade, A., El-Kazdadi, S., Taylor, A., Carpentier, J.: PROX-QP: Yet another quadratic programming solver for robotics and beyond. In: RSS 2022—Robotics: Science and Systems (2022). hal-03683733
Bemporad, A.: A numerically stable solver for positive semidefinite quadratic programs based on nonnegative least squares. IEEE Trans. Autom. Control 63(2), 525–531 (2018)
Cipolla, S., Gondzio, J.: Proximal stabilized interior point methods and low-frequency-update preconditioning techniques. J. Optim. Theory Appl. 197(3), 1061–1103 (2023)
De Marchi, A.: Augmented Lagrangian and Proximal Methods for Constrained Structured Optimization. Ph.D. thesis, University of the Bundeswehr Munich (2021)
De Marchi, A.: Augmented Lagrangian methods as dynamical systems for constrained optimization. In: 2021 60th IEEE Conference on Decision and Control (CDC) (2021)
De Marchi, A.: On a primal-dual Newton proximal method for convex quadratic programs. Comput. Optim. Appl. 81(2), 369–395 (2022)
De Marchi, A.: Implicit augmented Lagrangian and generalized optimization (2023). arXiv:2302.00363
De Marchi, A.: Regularized interior point methods for constrained optimization and control. IFAC-PapersOnLine. 22nd IFAC World Congress (2023)
De Marchi, A., Jia, X., Kanzow, C., Mehlitz, P.: Constrained composite optimization and augmented Lagrangian methods. Math. Program. 201(1), 863–896 (2023)
De Marchi, A., Mehlitz, P.: Local properties and augmented Lagrangians in fully nonconvex composite optimization (2023). arXiv:2309.01980
De Marchi, A., Themelis, A.: An interior proximal gradient method for nonconvex optimization (2022). arXiv:2208.00799
Gill, P.E., Robinson, D.P.: A primal-dual augmented Lagrangian. Comput. Optim. Appl. 51(1), 1–25 (2012)
Hermans, B., Themelis, A., Patrinos, P.: QPALM: a proximal augmented Lagrangian method for nonconvex quadratic programs. Math. Program. Comput. 14(3), 497–541 (2022)
Jallet, W., Bambade, A., Mansard, N., Carpentier, J.: PROX-NLP: a primal-dual augmented Lagrangian solver for nonlinear programming in robotics and beyond. In: 6th Legged Robots Workshop (2022). arXiv:2210.02109
Liao-McPherson, D., Kolmanovsky, I.: FBstab: a proximally stabilized semismooth algorithm for convex quadratic programming. Automatica 113, 108801 (2020)
Luque, F.J.: Asymptotic convergence analysis of the proximal point algorithm. SIAM J. Control. Optim. 22(2), 277–293 (1984)
Pougkakiotis, S., Gondzio, J.: An interior point-proximal method of multipliers for convex quadratic programming. Comput. Optim. Appl. 78(2), 307–351 (2021)
Stellato, B., Banjac, G., Goulart, P., Bemporad, A., Boyd, S.: OSQP: an operator splitting solver for quadratic programs. Math. Program. Comput. 12(4), 637–672 (2020)
Vanderbei, R.J.: Symmetric quasidefinite matrices. SIAM J. Optim. 5(1), 100–113 (1995)
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COAP 2022 Best Paper Prize. Comput Optim Appl 86, 1373–1375 (2023). https://doi.org/10.1007/s10589-023-00538-4
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DOI: https://doi.org/10.1007/s10589-023-00538-4