Presents an improved incremental learning algorithm based on KKT conditions.
提出了一种改进的基于KKT条件的增量学习算法。
The KKT system of the OPF is reformulated equivalently to a system of nonsmooth bounded constrained equations.
建立了与OPF 问题的KKT系统等价的带界约束的半光滑方程系统。
Therefore, its KKT conditions are different from those of the general equality constrained optimization problem.
转化后的问题要求其乘子是非负的，故其KKT条件与一般的等式约束优化问题不同。
When the object and constraint functions are continous, it shows the relations of KKT points and local saddle-points.
给出了齐次规划问题KKT点的一个等价性质，采用对约束函数k次方的方法得到齐次规划问题的一个局部鞍点。
By reformulating the KKT system as a constrained equation, the algorithm generates the search direction by solving a linear equation at each iteration.
通过将问题的KKT系统转化成一个约束方程，算法在每步迭代只需解一个线性方程组即可得到搜索方向。
A method of outlier detection in regression is proposed making use of the character of structure risk function and KKT condition in support vector regression.
利用支持向量回归算法中结构风险函数较好的平滑性以及KKT条件，提出一种回归中的异常值检测方法。
As the non-degeneracy condition holds and the smoothing parameter tends to zero, an S-stationary point of the MPCC problem is equivalent to a KKT point of the smoothing nonlinear programming.
当非退化条件成立和磨光参数趋于零时，证明了原问题的S -稳定点与磨光非线性规划的KKT点等价。
The KKT conditions of a nonlinear programming with linear inequality constrains can be transformed into a system of equations by NCP function. Then it is smoothed by Entropy smoothing function.
带不等式约束的非线性规划，其KKT条件可以通过NCP函数转化为一个非光滑的方程组，然后用熵光滑化函数光滑化，得到一个带参数的方程组。
The KKT conditions of a nonlinear programming with linear inequality constrains can be transformed into a system of equations by NCP function. Then it is smoothed by Entropy smoothing function.
带不等式约束的非线性规划，其KKT条件可以通过NCP函数转化为一个非光滑的方程组，然后用熵光滑化函数光滑化，得到一个带参数的方程组。
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