# Minimum Input Selection for Structural Controllability

arXiv: https://arxiv.org/abs/1407.2884 · HTML: https://mlsanity.com/paper/1407.2884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2014.

## Built on by (most cited first, 1 of 1)

- [Minimize Control Inputs for Strong Structural Controllability Using Reinforcement Learning with Graph Neural Network](https://mlsanity.com/paper/2402.16925/) (2024)

## Abstract (arXiv)

Given a linear system dotx = Ax, where A is an n × n matrix with m nonzero entries, we consider the problem of finding the smallest set of state variables to affect with an input so that the resulting system is structurally controllable. We further assume we are given a set of "forbidden state variables" F which cannot be affected with an input and which we have to avoid in our selection. Our main result is that this problem can be solved deterministically in O(n+m √n) operations.