Primal-dual methods

Primal-dual methods are algorithms used to solve complex optimization problems by working with two related perspectives: the primary problem (primal) and its associated constraints (dual). They iteratively improve solutions by adjusting estimates for both the original problem and the constraints simultaneously, ensuring they get closer to optimal. This approach helps efficiently find the best possible outcome, especially in large or complicated situations like network design or resource allocation, by leveraging the relationship between the primal and dual problems to guide the search for optimal solutions.