@article{stainforthetal,
  author = "D. A. Stainforth and T. Aina and C. Christensen and M. Collins and N. Faull and D. J. Frame and J. A. Kettleborough and S. Knight and A. Martin and J. M. Murphy and C. Piani and D. Sexton and L. A. Smith and R. A. Spicer and A. J. Thorpe and M. R. Allen",
  journal = "Nature",
  pages = "403--406",
  title = "Uncertainty in the predictions of the climate response to rising levels of greenhouse gases",
  volume = "433",
  year = "2005",
}



@unpublished{monads-tactics,
  author = "Andrew Martin and Jeremy Gibbons",
  note = "Submitted to MPC2002",
  title = "A Monadic Interpretation of Tactics",
}



@techreport{NA-07/20,
  abstract = "An Adaptive Cubic Overestimation (ACO) algorithm for unconstrained optimization, generalizing a method due to Nesterov & Polyak (Math. Programming 108, 2006, pp 177-205), is proposed. At each iteration of Nesterov & Polyak's approach, the global minimizer of a local cubic overestimator of the objective function is determined, and this ensures a significant improvement in the objective so long as the Hessian of the objective is Lipschitz continuous and its Lipschitz constant is available. The twin requirements of global model optimality and the availability of Lipschitz constants somewhat limit the applicability of such an approach, particularly for large-scale problems. However the promised powerful worst-case theoretical guarantees prompt us to investigate variants in which estimates of the required Lipschitz constant are refined and in which computationally-viable approximations to the global model-minimizer are sought. We show that the excellent global and local convergence properties and worst-case iteration complexity bounds obtained by Nesterov & Polyak are retained, and sometimes extended to a wider class of problems, by our ACO approach. Numerical experiments with small-scale test problems from the CUTEr set show superior performance of the ACO algorithm when compared to a trust-region implementation.",
  author = "Coralia Cartis and Nicholas I. M. Gould and Philippe L. Toint",
  institution = "Oxford University Computing Laboratory",
  month = "October",
  number = "NA-07/20",
  title = "Adaptive cubic overestimation methods for unconstrained optimization",
  year = "2007",
}



@techreport{NA-07/21,
  abstract = "We compute solutions for the Orr-Sommerfeld equations for the case of an asymmetric Poiseuille-like parallel flow. The calculations show that very small asymmetry has little effect on the prediction for linear instability of Poiseuille-like flow but that moderate asymmetry, such as found in channel flow near an elongated wall vortex, has a large effect and that instability can occur at much lower (less than 100) Reynolds numbers. We give some characterisation of the instability.",
  author = "Dick Kachuma and Ian Sobey",
  institution = "Oxford University Computing Laboratory",
  month = "November",
  number = "NA-07/21",
  title = "Linear instability of asymmetric Poiseuille flows",
  year = "2007",
}