本文介绍了一种基于递归函数的正交匹配追踪算法，通过优化稀疏信号的分解过程，在保证较高精度的同时大幅提升了计算效率。
Orthogonal Matching Pursuit (OMP) is an algorithm used for recursive function approximation, particularly in the context of wavelet decomposition. The OMP algorithm iteratively selects atoms from a dictionary to best approximate a target signal or function. In each iteration, it chooses the atom that has the highest correlation with the current residual and then updates the residual based on this selection until a stopping criterion is met. This process allows for efficient approximation in sparse representations, making it particularly useful in wavelet-based applications where signals can often be represented sparsely using appropriate wavelets.
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