Under mild conditions , we prove the global and superlinear convergence of the method 在较弱的条件下,得到了算法的全局收敛性及其超线性收敛性。
A feasible sqp algorithm with superlinear convergence for inequality constrained optimization 不等式约束优化一个具有超线性收敛的可行序列二次规划算法
Using the comparison principle , it is proved that the proposed method is of superlinear convergence 利用比较原理,间接证明该算法是一种具有超线性收敛性的近似牛顿法。
Furthermore , the global and superlinear convergence of the shamanskii modification of the newton method with the new line search are proved under the weaker conditions than those in ref [ 10 ] ( i . e . , 在本章中,我什1将邪a ? nans汕6修正牛顿法的迭代形式作了进一步的改进,改进后的sha 。 a 。
Lc1 unconstrained optimization problem was discussed in the second chapter , giving a new trust region method and proving its global convergence and superlinear convergence under some mild conditions 给出了一个新的信赖域算法,并在一定的条件下证明了算法的全局收敛性和局部超线性收敛性。