1·The proposed methods are proved to possess the superlinear and quadratic convergence.
该方法被证明具有超线性和二次收敛性。
2·The local superlinear and quadratic convergence of this two models under some mild conditions without the strict complementary condition are analysed and proved.
详细分析和论证两个模型的局部超线性收敛性及二次收敛性条件，其中并不需要严格互补条件。
3·For the basic differential evolution algorithm, the increasing quadratic function crossover operator was added to increase the convergence speed.
在基本差分进化算法中，融入递增二次函数交叉算子以增加算法的收敛速度。
4·Sequential quadratic programming (SQP) method is an efficient method for solving smooth constrained optimization problems because of its fast convergence rate.
由于序列二次规划（SQP）算法具有快速收敛速度，所以它是求解光滑约束优化问题的有效方法之一。
5·We present a new branchandbound algorithm for solving quadratic programming problem with quadratic constraints, and analyze the convergence of the algorithm.
提出了一种解带有二次约束二次规划问题的新的分枝定界算法对该算法进行了收敛性分析。
6·The rate of convergence is nearly quadratic, and is quadratic under some additional conditions.
该迭代算法具有几乎二次的收敛率，在某些条件下达到了二次收敛。
7·In view of its features, we use sequence quadratic programming planning to solve the problem effectively, in which convergence carries through fast and the stability is fine.
根据问题的特点，本文采用了收敛速度快、稳定性好的序列二次规划法对模型进行了有效求解。
8·In this paper, a lower approximating algorithm of large-scale concave quadratic programming in unbounded domain is constructed. The convergence of the algorithm is discussed.
本文给出了无界域上大规模凹二次规划的一种下逼近算法，并证明了算法的收敛性。
9·Under new control conditions, we prove convergence of the quadratic minimization problem, which improves the recent results by Xu about quadratic optimization.
在新的控制条件下，证明了二次型极小化问题的迭代算法的有效性，所得结果改进了徐洪坤关于二次型优化的最新结果。