A method turning a solution of a continuous optimization relaxation into a random feasible solution of a discrete problem. An expected objective bound can give an approximation guarantee. Gaussian hyperplane rounding and Rademacher rounding for a semidefinite relaxation are different examples.
Randomized rounding is an algorithmic technique often used in the context of approximation algorithms and integer programming. It is particularly useful for dealing with problems where one needs to convert a fractional solution (obtained from solving a linear relaxation of an integer programming problem) into a feasible integer solution, while maintaining a certain level of optimality. ### Overview: 1. **Linear Relaxation**: In integer programming, the objective is to find integer solutions to certain optimization problems.
New to topics? Read the docs here!