Approximation algorithms

LP based Approximation algorithms

Rephrase (maybe NP hard) problem as Integer Programming problem; Make LP relaxation; solve in polytime; translate solution by rounding; make \((\del, \eps)\) approximation guarantees. Rounding choices: To closest integer, or randomized rounding.

Vertex cover problem

G=(V,E). IP: Vars \(v_{i}\) = 0 or 1, \(\forall (i,j) \in E, v_{i}+v_{j} \geq 1\), \(\min \sum v_{i} = ?\); LP relaxation: \(\hat{v}{i} \in [0,1]\); solution \(\sum \hat{v}{i} \leq opt \leq \sum v_{i}\); round to nearest int to get approx soln \(\set{v_{i}}\); as \(v_{i} \leq 2 \hat{v}{i}\): \(\sum v{i} \leq 2 opt\).

Max SAT.

\tbc

Semidefinite programming based approximation algs

\tbc