Stochastic Optimization

Stochastic optimization is a method used to find the best solution in situations where there is uncertainty or randomness involved. Instead of relying on fixed data, it uses probabilistic models to account for variability, making decisions that are effective on average or under uncertain conditions. This approach is common in complex problems like supply chain planning or machine learning, where exact data is unavailable or too costly to obtain. By incorporating randomness, stochastic optimization helps identify solutions that are robust and perform well across various possible scenarios, rather than optimizing for a single, fixed outcome.