We present an algorithm for large-scale equality constrained optimization. The method is based on a characterization of inexact sequential quadratic programming (SQP) steps that ca...
Abstract. Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second deri...
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
We present a new approximation method called value extrapolation for Markov processes with large or infinite state spaces. The method can be applied for calculating any performan...
Abstract We present a matrix-free line search algorithm for large-scale equality constrained optimization that allows for inexact step computations. For sufficiently convex problem...