# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms

arXiv: https://arxiv.org/abs/2006.04411 · HTML: https://mlsanity.com/paper/2006.04411/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique](https://mlsanity.com/paper/2211.01443/) (2022)
- [Fine-Grained Complexity and Algorithms for the Schulze Voting Method](https://mlsanity.com/paper/2103.03959/) (2021)
- [Participatory Budgeting With Multiple Degrees of Projects And Ranged Approval Votes](https://mlsanity.com/paper/2305.10972/) (2023)
- [A short review on the maximum clique problem algorithms with classical, AI, and quantum methods](https://mlsanity.com/paper/2403.09742/) (2024)
- [Fast decision tree learning solves hard coding-theoretic problems](https://mlsanity.com/paper/2409.13096/) (2024)

## Abstract (arXiv)

Parameterization and approximation are two popular ways of coping with NP-hard problems. More recently, the two have also been combined to derive many interesting results. We survey developments in the area both from the algorithmic and hardness perspectives, with emphasis on new techniques and potential future research directions.
