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。
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。
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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