# A totally unimodular view of structured sparsity

arXiv: https://arxiv.org/abs/1411.1990 · HTML: https://mlsanity.com/paper/1411.1990/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback](https://mlsanity.com/paper/2205.07217/) (2022)
- [Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting](https://mlsanity.com/paper/2508.00523/) (2025)

## Abstract (arXiv)

This paper describes a simple framework for structured sparse recovery based on convex optimization. We show that many structured sparsity models can be naturally represented by linear matrix inequalities on the support of the unknown parameters, where the constraint matrix has a totally unimodular (TU) structure. For such structured models, tight convex relaxations can be obtained in polynomial time via linear programming. Our modeling framework unifies the prevalent structured sparsity norms in the literature, introduces new interesting ones, and renders their tightness and tractability arguments transparent.