On solving the densest k-subgraph problem on large graphs

Research output: Contribution to journalArticleScientificpeer-review


The densest k-subgraph problem is the problem of finding a k-vertex subgraph of a graph with the maximum number of edges. In order to solve large instances of the densest k-subgraph problem, we introduce two algorithms that are based on the random coordinate descent approach. Although it is common use to update at most two random coordinates simultaneously in each iteration of an algorithm, our algorithms may simultaneously update many coordinates. We show the benefit of updating more than two coordinates simultaneously for solving the densest k-subgraph problem, and solve large problem instances with up to 2^{15} vertices.
Original languageEnglish
JournalOptimization Methods and Software
Publication statusE-pub ahead of print - Mar 2019


  • densest k-subgraph problem
  • random coordinate descent algorithm
  • large graphs

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