Deterministic Optimization

Deterministic optimization aims to find the best solution to a problem with known, fixed parameters, a crucial task across numerous fields. Current research focuses on extending deterministic optimization to handle uncertainty, often by transforming stochastic problems into deterministic equivalents using techniques like sample average approximation or conformal predictive programming. These methods, sometimes integrated with machine learning models such as neural networks, are applied to diverse areas including energy grids, resource allocation, and trajectory planning, improving decision-making under uncertainty and enhancing efficiency. The development of robust and efficient algorithms for these approaches is a key area of ongoing investigation, with a focus on improving computational tractability and decision quality.