LPs and Convex Programming Relaxations and Rounding for Stochastic Problems
berkeley.edu
LPs and Convex Programming Relaxations and Rounding for Stochastic Problems
We will survey some ways that linear programming relaxations can capture optimal strategies in (discrete) stochastic optimization problems. These problems are often NP-hard or worse, and we will discuss techniques used to develop approximation algorithms for them. Examples include stochastic knapsack and some budgeted multi-armed bandit problems.
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