Online Stochastic

Online stochastic optimization focuses on developing algorithms that efficiently learn optimal decisions in dynamic environments where data arrives sequentially and is inherently uncertain. Current research emphasizes gradient-based methods, including online gradient descent and its variants, often adapted for specific problem structures like quasar-convexity or incorporating prior knowledge (e.g., approximate system dynamics). These advancements are crucial for addressing challenges in diverse fields, such as robotics, energy management, and online advertising, where real-time decision-making under uncertainty is paramount. The development of robust and efficient algorithms with provable regret bounds remains a central theme.