Every graph can be partitioned into a universally bounded number of fixed edge pieces, each with ordinary random-graph containment threshold at most a universal constant times the original graph’s integral expectation threshold.
Every graph can be partitioned into a universally bounded number of fixed edge pieces, each with ordinary random-graph containment threshold at most a universal constant times the original graph’s integral expectation threshold.
Every graph can be partitioned into a universally bounded number of fixed edge pieces, each with ordinary random-graph containment threshold at most a universal constant times the original graph’s integral expectation threshold.
If correct, resolves the graph-decomposition conjecture and removes the logarithmic threshold loss for each piece.
Can every graph be split into a bounded number of pieces whose thresholds match its integral expectation threshold?
Publication dates and editorial changes.
OpenAI reports graph decomposition at the integral expectation threshold
Allow optional Google Analytics to help improve AIProofWatch? Privacy details.
