Performance of various BFGS and DFP implementations with limited precision second order information.
This paper supports the claim that there is no discernible advantage in choosing factorised implementations (over non–factorised implementations) of BFGS methods when approximate Hessian information is available to full machine precision. However the results presented in this paper show that a factorisation strategy has clear advantages when approximate Hessian information is available only to limited precision. These results show that a conjugate directions factorisation outperforms the other methods considered in this paper (including Cholesky factorisation) for both BFGS and DFP formulae.
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