| 1. | 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。 |
| 2. | 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 |
| 3. | 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。 |
| 4. | 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 。 |
| 5. | 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。 |
| 6. | We then develop a bfgs method for solving the nonsmooth equation . the method possess some descent property . under mild conditions , we establish the global and superlinear convergence of the proposed method
在此基础上，我们提出一种单调下降的线性搜索，进而提出求解该非光滑方程组的具有单调下降性的bfgs算法。 |
| 7. | The general shamanskii modification of the newton method is defined by the iteration and the global and superlinear convergence of the general shamanskii modification of the newton method are proved in this dissertation
我们不仅证明了改进的sha一mansk灯修正牛顿法的全局收敛性，而且也证明该方法具有超线性收敛速度 |
| 8. | In chapter 2 . we give a class of new algorithms for nonlinear programming problems with linear constrained by combining the gradient projection method with non - quasi - newton method which was given in paper [ 2 ] . it ' s global convergence and the superlinear convergence are proved under suitable conditions
在第二章中我们将梯度投影与文[ 2 ]中的非拟牛顿法相结合，给出了求解线性约束非线性优化问题的一类梯度投影非拟牛顿算法。 |
| 9. | In chapter 3 , we give a class of new algorithms with inexact search for nonlinear programming problems with linear constrained by combining the generalized projection method with non - quasi - newton method . it ' s global convergence and the superlinear convergence are proved under suitable conditions
新算法推广了文[ 1 ， 2 ]中的结果。在第三章中我们将广义投影算法与非拟牛顿法相结合，给出了求解线性约束非线性优化问题的一类广义投影非拟牛顿算法。 |
| 10. | In the third chapter we discuss lc1 constrained optimization problem . to solve it , we turn it into nonsmooth equations , utilizing inexact theory we give an inexact generalized newton ' s method and under some mild conditions we prove that it is global convergence and superlinear convergence
首先将其约束问题的求解转化为非光滑方程组的求解，然后利用不完全求解理论给出了一个非精确的广义牛顿算法，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并给出了lc ~ 1非线性约束问题的收敛性条件。 |
