Gadgets, Approximation, and Linear Programming (extended abstract)

The authors present a linear-programming based method for finding "gadgets", i. e. , combinatorial structures reducing constraints of one optimization problem to constraints of another. A key step in this method is a simple observation which limits the search space to a finite one. Using this new method they present a number of new, computer-constructed gadgets for several different reductions. This method also answers the question of how to prove the optimality of gadgets-they show how LP duality gives such proofs. The new gadgets improve hardness results for MAX CUT and MAX DICUT, showing that approximating these problems to within factors of 60/61 and 44/45 respectively is NP-hard (improving upon the previous hardness of 71/72 for both problems). They also use the gadgets to obtain an improved approximation algorithm for MAX 3SAT which guarantees an approximation ratio of 0. 801, This improves upon the previous best bound of 0. 7704.

Authors

• Luca Trevisan 0001
• Gregory B. Sorkin
• Madhu Sudan 0001
• David P. Williamson

Keywords

• Linear approximation
• Linear programming
• Constraint optimization
• Cost function
• Approximation algorithms
• Remuneration
• Optimization methods
• Concrete
• Optimization Problem
• Estimation Algorithm
• Hardness Results
• Lower Bound
• Weight Vector
• Primary Variables
• Inequality Constraints
• Auxiliary Variables
• Disjunction
• Polynomial-time Algorithm
• Constraint Satisfaction Problem
• Function Fi