Convergent Algorithm

Convergent algorithms aim to develop provably efficient and reliable methods for solving complex optimization problems arising in diverse fields like machine learning, signal processing, and game theory. Current research emphasizes developing convergence guarantees for algorithms applied to non-convex objectives, including those employing techniques like stochastic gradient descent, plug-and-play methods with deep denoisers, and federated learning approaches. This work is crucial for advancing the reliability and scalability of machine learning models and improving the performance of numerous applications that rely on efficient optimization.