J
Jaroslaw Byrka
Researcher at University of Wrocław
Publications - 88
Citations - 2398
Jaroslaw Byrka is an academic researcher from University of Wrocław. The author has contributed to research in topics: Approximation algorithm & Facility location problem. The author has an hindex of 21, co-authored 84 publications receiving 2162 citations. Previous affiliations of Jaroslaw Byrka include Eindhoven University of Technology & École Polytechnique Fédérale de Lausanne.
Papers
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Proceedings ArticleDOI
An improved LP-based approximation for steiner tree
TL;DR: This paper improves the approximation factor for Steiner tree, developing an LP-based approximation algorithm based on a, seemingly novel, iterative randomized rounding technique and shows that the integrality gap of the LP is at most $1.55, hence answering to the mentioned open question.
Journal ArticleDOI
Steiner Tree Approximation via Iterative Randomized Rounding
TL;DR: This article presents an LP-based approximation algorithm for Steiner tree with an improved approximation factor based on a, seemingly novel, iterative randomized rounding technique, and shows that the integrality gap of the LP is at most 1.55, answering the mentioned open question.
Posted Content
An Improved Approximation for $k$-median, and Positive Correlation in Budgeted Optimization
TL;DR: In this paper, the authors improve upon Li-Svensson's approximation ratio for the k-median problem from 2.732 + \epsilon to 2.675 + ǫ.
Journal ArticleDOI
An Optimal Bifactor Approximation Algorithm for the Metric Uncapacitated Facility Location Problem
Jaroslaw Byrka,Karen Aardal +1 more
TL;DR: In this article, the authors obtained a 1.5-approximation algorithm for the metric uncapacitated facility location (UFL) problem, which is the best known algorithm.
Proceedings ArticleDOI
An improved approximation for k-median, and positive correlation in budgeted optimization
TL;DR: This work improves upon Li-Svensson’s approximation ratio for k-median by developing an algorithm that improves upon various aspects of their work and develops algorithms that guarantee the known properties of dependent rounding but also have nearly bestpossible behavior—near-independence, which generalizes positive correlation—on “small” subsets of the variables.