Subexponential LPs Approximate Max-Cut

Subexponential LPs Approximate Max-Cut

Explore a groundbreaking approach to approximating the Max-Cut problem using subexponential linear programs in this 26-minute IEEE conference talk. Delve into combinatorial optimization, linear and semidefinite programming, and their comparative analysis. Learn how LPs can effectively approximate Max Cut and examine additional discrete optimization problems. Follow the speakers through an engaging narrative, including a plot twist involving refutation in pseudorandom graphs, before reaching the conclusion of LP approximation in any graph. Gain insights from a high-level proof overview and walk away with a deeper understanding of this innovative solution in graph theory and optimization.