Approximating Node-Weighted k-MST on Planar Graphs

12/31/2017
by   Jaroslaw Byrka, et al.
0

We study the problem of finding a minimum weight connected subgraph spanning at least k vertices on planar, node-weighted graphs. We give a (4+)-approximation algorithm for this problem. In the process, we use the recent LMP primal-dual 3-approximation for the node-weighted prize-collecting Steiner tree problem and the Lagrangian relaxation. In particular, we improve the procedure of picking additional vertices given by Sadeghian by taking a constant number of recursive steps and utilizing the limited guessing procedure of Arora and Karakostats. We argue that our approach can be interpreted as a generalization of a result by Könemann et al. Together with a result by Mestre this implies that our bound is essentially best possible among algorithms that utilize an LMP algorithm for the Lagrangian relaxation as a black box.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/10/2021

Hitting Weighted Even Cycles in Planar Graphs

A classical branch of graph algorithms is graph transversals, where one ...
research
12/02/2019

PTAS for Steiner Tree on Map Graphs

We study the Steiner tree problem on map graphs, which substantially gen...
research
10/16/2019

Node-Weighted Network Design in Planar and Minor-Closed Families of Graphs

We consider node-weighted survivable network design (SNDP) in planar gra...
research
06/02/2023

Revisiting Garg's 2-Approximation Algorithm for the k-MST Problem in Graphs

This paper revisits the 2-approximation algorithm for k-MST presented by...
research
11/30/2017

Approximating Connected Safe Sets in Weighted Trees

For a graph G and a non-negative integral weight function w on the verte...
research
10/09/2019

Minimum Cuts in Surface Graphs

We describe algorithms to efficiently compute minimum (s,t)-cuts and glo...
research
10/19/2017

A Primal-Dual based Distributed Approximation Algorithm for Prize Collecting Steiner Tree

Constructing a steiner tree of a graph is a fundamental problem in many ...

Please sign up or login with your details

Forgot password? Click here to reset