On the BFGS Method for Unconstrained Optimization
Quasi-Newton methods form the basis of many effective methods for unconstrained and constrained optimization. As the iterations proceed, a quasi-Newton method incorporates new curvature information by performing a low-rank update to a matrix that serves as an approximation to a Hessian matrix of second derivatives. In the years following the publication of the Davidon-Fletcher-Powell (DFP) … Read more