论文标题
Swagger：跨组内部和跨组的稀疏性，以进行一般估计和恢复
SWAGGER: Sparsity Within and Across Groups for General Estimation and Recovery
论文作者
论文摘要
在许多领域，促进结构化解决方案的惩罚功能或正规化术语在许多领域都引起了人们的极大兴趣。在这项工作中提出的是一种非凸结构的稀疏性惩罚，可在矢量中的任意重叠组中促进一项比较。这允许人们在解决方案中针对优化问题的组件之间实施相互的排他性。我们显示了多个示例用例（包括总变异变体），证明了IT与其他正规化器之间的协同作用，并提出了一种算法来有效解决受提议罚款正式或约束的问题。
Penalty functions or regularization terms that promote structured solutions to optimization problems are of great interest in many fields. Proposed in this work is a nonconvex structured sparsity penalty that promotes one-sparsity within arbitrary overlapping groups in a vector. This allows one to enforce mutual exclusivity between components within solutions to optimization problems. We show multiple example use cases (including a total variation variant), demonstrate synergy between it and other regularizers, and propose an algorithm to efficiently solve problems regularized or constrained by the proposed penalty.
