Approximate Constraint Satisfaction Requires Large LP Relaxations
Siu On Chan, James R. Lee, Prasad Raghavendra, David Steurer · Journal of the ACM · 2013
We prove super-polynomial lower bounds on the size of linear programming relaxations for approximation versions of constraint satisfaction problems. We show that for these problems, polynomial-sized linear programs are exactly as powerful as programs arising from a constant number of rounds of the Sherali-Adams hierarchy. In particular, any polynomial-sized linear program for MAX CUT has an integrality gap of 1/2 and any such linear program for MAX 3-SAT has an integrality gap of 7/8.