Rachael Tappenden

Senior LecturerRachael Tappenden

Jack Erskine 714
Internal Phone: 92437


Research Interests

My research interests lie in the broad area of optimization and numerical linear algebra.

Recent Publications

  • Sadiev A., Borodich E., Beznosikov A., Dvinskikh D., Chezhegov S., Tappenden R., Takáč M. and Gasnikov A. (2022) Decentralized personalized federated learning: Lower bounds and optimal algorithm for all personalization modes. EURO Journal on Computational Optimization 10 http://dx.doi.org/10.1016/j.ejco.2022.100041.
  • Coope ID. and Tappenden R. (2021) Gradient and diagonal Hessian approximations using quadratic interpolation models and aligned regular bases. Numerical Algorithms 88(2): 767-791. http://dx.doi.org/10.1007/s11075-020-01056-8.
  • Jahani M., Gudapati NVC., Ma C., Tappenden R. and Takáč M. (2021) Fast and safe: accelerated gradient methods with optimality certificates and underestimate sequences. Computational Optimization and Applications 79(2): 369-404. http://dx.doi.org/10.1007/s10589-021-00269-4.
  • Coope I. and Tappenden R. (2019) Efficient calculation of regular simplex gradients. Computational Optimization and Applications 72(3): 561-588. http://dx.doi.org/10.1007/s10589-019-00063-3.
  • Coup S., Vetrova V., Frank E. and Rachael T. (2019) Domain specific transfer learning using image mixing and and stochastic image selection. In.