Skip to main content
Log in

COAP 2022 Best Paper Prize

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. 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

  2. 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)

    Article  MATH  Google Scholar 

  3. Cipolla, S., Gondzio, J.: Proximal stabilized interior point methods and low-frequency-update preconditioning techniques. J. Optim. Theory Appl. 197(3), 1061–1103 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  4. De Marchi, A.: Augmented Lagrangian and Proximal Methods for Constrained Structured Optimization. Ph.D. thesis, University of the Bundeswehr Munich (2021)

  5. De Marchi, A.: Augmented Lagrangian methods as dynamical systems for constrained optimization. In: 2021 60th IEEE Conference on Decision and Control (CDC) (2021)

  6. De Marchi, A.: On a primal-dual Newton proximal method for convex quadratic programs. Comput. Optim. Appl. 81(2), 369–395 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  7. De Marchi, A.: Implicit augmented Lagrangian and generalized optimization (2023). arXiv:2302.00363

  8. De Marchi, A.: Regularized interior point methods for constrained optimization and control. IFAC-PapersOnLine. 22nd IFAC World Congress (2023)

  9. De Marchi, A., Jia, X., Kanzow, C., Mehlitz, P.: Constrained composite optimization and augmented Lagrangian methods. Math. Program. 201(1), 863–896 (2023)

    Article  MathSciNet  MATH  Google Scholar 

  10. De Marchi, A., Mehlitz, P.: Local properties and augmented Lagrangians in fully nonconvex composite optimization (2023). arXiv:2309.01980

  11. De Marchi, A., Themelis, A.: An interior proximal gradient method for nonconvex optimization (2022). arXiv:2208.00799

  12. Gill, P.E., Robinson, D.P.: A primal-dual augmented Lagrangian. Comput. Optim. Appl. 51(1), 1–25 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  13. Hermans, B., Themelis, A., Patrinos, P.: QPALM: a proximal augmented Lagrangian method for nonconvex quadratic programs. Math. Program. Comput. 14(3), 497–541 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  14. 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

  15. Liao-McPherson, D., Kolmanovsky, I.: FBstab: a proximally stabilized semismooth algorithm for convex quadratic programming. Automatica 113, 108801 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  16. Luque, F.J.: Asymptotic convergence analysis of the proximal point algorithm. SIAM J. Control. Optim. 22(2), 277–293 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  17. Pougkakiotis, S., Gondzio, J.: An interior point-proximal method of multipliers for convex quadratic programming. Comput. Optim. Appl. 78(2), 307–351 (2021)

    Article  MathSciNet  MATH  Google Scholar 

  18. 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)

    Article  MathSciNet  MATH  Google Scholar 

  19. Vanderbei, R.J.: Symmetric quasidefinite matrices. SIAM J. Optim. 5(1), 100–113 (1995)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

COAP 2022 Best Paper Prize. Comput Optim Appl 86, 1373–1375 (2023). https://doi.org/10.1007/s10589-023-00538-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-023-00538-4

Navigation