We introduce a new algorithm promoting sparsity called {\it Support Exploration Algorithm (SEA)} and analyze it in the context of support recovery/model selection problems.The algorithm can be interpreted as an instance of the {\it straight-through estimator (STE)} applied to the resolution of a sparse linear inverse problem. SEA uses a non-sparse exploratory vector and makes it evolve in the input space to select the sparse support. We put to evidence an oracle update rule for the exploratory vector and consider the STE update. The theoretical analysis establishes general sufficient conditions of support recovery. The general conditions are specialized to the case where the matrix $A$ performing the linear measurements satisfies the {\it Restricted Isometry Property (RIP)}.Experiments show that SEA can efficiently improve the results of any algorithm. Because of its exploratory nature, SEA also performs remarkably well when the columns of $A$ are strongly coherent.

翻译：我们提出了一种新的促进稀疏性的算法，称为支持探索算法（SEA），并在支持恢复/模型选择问题的背景下对其进行了分析。该算法可被解释为直通估计器（STE）在解决稀疏线性逆问题中的一个实例。SEA使用一个非稀疏的探索向量，并使其在输入空间中演化以选择稀疏支持。我们为探索向量提出了一个Oracle更新规则，并考虑了STE更新。理论分析建立了支持恢复的一般充分条件。这些一般条件被特化到执行线性测量的矩阵$A$满足限制等距性质（RIP）的情况。实验表明，SEA能够高效地改进任何算法的结果。由于其探索性质，当$A$的列强相干时，SEA也表现异常出色。