theoretical-computer-science / Approximation algorithms; semidefinite relaxations and metric embeddings
The Goemans-Linial integrality gap for uniform sparsest cut is nearly square-root logarithmic
Theorem 1.1: there are an absolute c>0 and uniform-demand instances on nj→∞ vertices with Goemans-Linial integrality gap at least clognj/(loglognj)3. This improves the Kane-Meka bound exp(Ω(loglogn)) to within (loglogn)3 of the ARV upper bound. It is an existential sequence of sizes, not a bound at every n; it does not determine whether the gap is exactly Θ(logn), and it concerns the basic SDP, not Sherali-Adams or Lasserre strengthenings.
Theorem 1.1: there are an absolute c>0 and uniform-demand instances on nj→∞ vertices with Goemans-Linial integrality gap at least clognj/(loglognj)3. This improves the Kane-Meka bound exp(Ω(loglogn)) to within (loglogn)3 of the ARV upper bound. It is an existential sequence of sizes, not a bound at every n; it does not determine whether the gap is exactly…
Theorem 1.1: there are an absolute c>0 and uniform-demand instances on nj→∞ vertices with Goemans-Linial integrality gap at least clognj/(loglognj)3. This improves the Kane-Meka bound exp(Ω(loglogn)) to within (loglogn)3 of the ARV upper bound. It is an existential sequence of sizes, not a bound at every n; it does not determine whether the gap is exactly Θ(logn), and it concerns the basic SDP, not Sherali-Adams or Lasserre strengthenings.
Theorem 1.1: there are an absolute c>0 and uniform-demand instances on nj→∞ vertices with Goemans-Linial integrality gap at least clognj/(loglognj)3. This improves the Kane-Meka bound exp(Ω(loglogn)) to within (loglogn)3 of the ARV upper bound. It is an existential sequence of sizes, not a bound at every n; it does not determine whether the gap is exactly Θ(logn), and it concerns the basic SDP, not Sherali-Adams or Lasserre strengthenings.