The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algebra techniques. Hence, it is appropriate for solving large linear systems. The analysis highlights its suitability within the class of well posed problems.

中文翻译：

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自投影匹配追踪算法分析

提出了正交匹配追踪贪婪策略低内存实现的收敛性和数值分析，该策略称为自投影匹配追踪。与直接线性代数技术相比，此方法提供了一种迭代方法来解决最小二乘问题，且所需存储量少得多。因此，适用于求解大型线性系统。分析强调了它在适当提出的问题类别中的适用性。