{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreiazon7jz52mz6drlvllufhqpmqhx3zdrcld57s372otvvmgo7uua4",
    "uri": "at://did:plc:3fychdutjjusoqeq24ljch6q/app.bsky.feed.post/3mifxlnbmjjb2"
  },
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreiflo6xt7is6b2iafwghkjahlgggocme5jwjsbeuqqwcywuvjhmszm"
    },
    "mimeType": "image/png",
    "size": 24783
  },
  "path": "/abs/2603.29582v1",
  "publishedAt": "2026-04-01T00:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Vincent Cohen-Addad",
    "Marina Drygala",
    "Nathan Klein",
    "Ola Svensson"
  ],
  "textContent": "**Authors:** Vincent Cohen-Addad, Marina Drygala, Nathan Klein, Ola Svensson\n\nThe Weighted Tree Augmentation Problem (WTAP) is a fundamental network design problem where the goal is to find a minimum-cost set of additional edges (links) to make an input tree 2-edge-connected. While a 2-approximation is standard and the integrality gap of the classic Cut LP relaxation is known to be at least 1.5, achieving approximation factors significantly below 2 has proven challenging. Recent advances of Traub and Zenklusen using local search culminated in a ratio of $1.5+ε$, establishing the state-of-the-art. In this work, we present a randomized approximation algorithm for WTAP with an approximation ratio below 1.49. Our approach is based on designing and rounding a strong linear programming relaxation for WTAP which incorporates variables that represent subsets of edges and the links used to cover them, inspired by lift-and-project methods like Sherali-Adams.",
  "title": "A Strong Linear Programming Relaxation for Weighted Tree Augmentation"
}