Sparse Vector Recovery Dataset

该数据集涉及在一个线性子空间中恢复植入的稀疏向量问题，其中稀疏向量是在多种条件下进行采样的。数据集是在不同的采样过程下生成的，包括接受/拒绝、伯努利-高斯、修正的伯努利-高斯以及伯努利-拉德马赫过程。该数据集的任务是稀疏向量恢复。

This dataset addresses the problem of recovering implanted sparse vectors within a linear subspace, where the sparse vectors are sampled under diverse conditions. The dataset is generated via multiple sampling processes, including accept-reject, Bernoulli-Gaussian, modified Bernoulli-Gaussian, and Bernoulli-Rademacher processes. The core task of this dataset is sparse vector recovery.