Distributionally robust optimization is a framework that seeks to find solutions to optimization problems that remain effective under a range of possible probability distributions for uncertain parameters. This approach accounts for model uncertainty by considering worst-case scenarios, ensuring that solutions are resilient and reliable, regardless of how the true distribution of uncertainty may deviate from the assumed one. It integrates the principles of robust optimization with statistical insights, allowing for more informed decision-making in uncertain environments.
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