robust optimization

What are Common Techniques Used in Robust Optimization?


Several techniques are used in robust optimization, including:
Stochastic Programming: Incorporates randomness directly into the optimization model.
Worst-Case Analysis: Focuses on optimizing the worst possible scenario.
Sensitivity Analysis: Examines how the variation in input parameters affects the output.
Monte Carlo Simulations: Uses repeated random sampling to account for uncertainty.
These techniques help in developing catalysts that are less sensitive to variations, leading to more stable and predictable performance.

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