A Global Analysis of the Primal-Dual Method for Pliable Families

August 30, 2023 Β· Declared Dead Β· πŸ› Conference on Integer Programming and Combinatorial Optimization

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Ishan Bansal arXiv ID 2308.15714 Category cs.DS: Data Structures & Algorithms Citations 8 Venue Conference on Integer Programming and Combinatorial Optimization Last Checked 4 months ago
Abstract
We study a core algorithmic problem in network design called ${F}$-augmentation that involves increasing the connectivity of a given family of cuts ${F}$. Over 30 years ago, Williamson et al. (STOC `93) provided a 2-approximation primal-dual algorithm when ${F}$ is a so-called uncrossable family but extending their results to families that are non-uncrossable has remained a challenging question. In this paper, we introduce the novel concept of the crossing density of a set family and show how this opens up a completely new approach to analyzing primal-dual algorithms. We study pliable families, a strict generalization of uncrossable families introduced by Bansal et al. (ICALP `23), and provide the first approximation algorithm for ${F}$-augmentation of general pliable families. We also improve on the results in Bansal et al. (ICALP `23) by providing a 6-approximation algorithm for the ${F}$-augmentation problem when ${F}$ is a family of near min-cuts. This immediately improves approximation factors for the Capacitated Network Design Problem. Finally, we study the $(p,3)$-flexible graph connectivity problem. By carefully analyzing the structure of feasible solutions and using the techniques developed in this paper, we provide the first constant factor approximation algorithm for this problem exhibiting an 12-approximation algorithm.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Data Structures & Algorithms

Died the same way β€” πŸ‘» Ghosted