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提出一种有效的Ｕ－Ｄ分解ＤＦＰ和ＢＦＧＳ算法．该算法解决了Ｈ阵的正定性问题，保证了算法的数值稳定性，并大大提高了计算效率．对Ｈ阵的计算量分析表明，该算法的计算效率比普通方法高２０％，比普通平方根方法高０．４ｎ（ｎ为Ｈ阵维数）倍．神经网络训练的应用表明，新算法比普通ＤＰＰ和ＢＦＧＳ方法更有效、更准确．
An efficient U-D decomposition algorithm for DFP and BFGS is proposed. The algorithm solves the positive definite problem of H matrix, guarantees the numerical stability of the algorithm and greatly improves the computational efficiency. The computation of H matrix shows that the computational efficiency of the algorithm is 20% higher than that of the conventional method and 0.4n higher than the ordinary square root method (n is the dimension of H matrix). The application of neural network training shows that the new algorithm is more effective and accurate than the ordinary DPP and BFGS methods.