Solving Sparse Random Instances of Max Cut and Max 2-CSP in Linear Expected Time

Abstract. We show that a maximum cut of a random graph below the giantcomponent threshold can be found in linear space and linear expected time by a simple algorithm. In fact, the algorithm solves a more general class of problems, namely binary 2-variable constraint satisfaction problems. In addition to Max Cut, such Max 2-CSPs encompass Max Dicut, Max 2-Lin, Max 2-Sat, Max-Ones-2-Sat, maximum independent set, and minimum vertex cover. We show that if a Max 2-CSP instance has an "underlying" graph which is a random graph G G(n, c/n), then the instance is solved in linear expected

• Type: Journal
• Year: 2006
• Where: CPC
• Authors: Alexander D. Scott, Gregory B. Sorkin
• Keywords: CPC 2006, Max 2-csp, Max 2-CSP Instance, Random Graph