Finite Time Convergence

Finite-time convergence focuses on developing algorithms that guarantee reaching a solution within a predetermined timeframe, rather than relying on asymptotic convergence. Current research emphasizes achieving this in various contexts, including reinforcement learning (using actor-critic methods and temporal difference learning), multi-agent systems, and optimization problems (employing stochastic gradient descent variants and model predictive control). This research is significant because finite-time guarantees enhance the reliability and efficiency of algorithms across diverse fields, from robotics and control systems to machine learning and distributed optimization.