A high-performance sparse model is very important for processing high-dimensional data. Therefore, based on the quadratic approximate greed pursuit (QAGP) method, we can make full use of the information of the quadratic lower bound of its approximate function to get the relaxation quadratic approximate greed pursuit (RQAGP) method. The calculation process of the RQAGP method is to construct two inexact quadratic approximation functions by using the m-strongly convex and L-smooth characteristics of the objective function and then solve the approximation function iteratively by using the Iterative Hard Thresholding (IHT) method to get the solution of the problem. The convergence analysis is given, and the performance of the method in the sparse logistic regression model is verified on synthetic data and real data sets. The results show that the RQAGP method is effective.

中文翻译：

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基于稀疏学习的松弛二次逼近贪心追踪法

高性能的稀疏模型对于处理高维数据非常重要。因此，在二次近似贪婪追踪（QAGP）方法的基础上，充分利用其近似函数的二次下界信息，得到松弛二次近似贪婪追踪（RQAGP）方法。RQAGP方法的计算过程是利用下式构造两个不精确的二次逼近函数米- 强凸且L- 目标函数的平滑特性，然后利用迭代硬阈值（IHT）方法迭代求解近似函数，得到问题的解。给出了收敛性分析，并在合成数据和真实数据集上验证了该方法在稀疏逻辑回归模型中的性能。结果表明RQAGP方法是有效的。