Sharp Bound

"Sharp bounds" research focuses on establishing precise, mathematically rigorous limits on the performance or behavior of various algorithms and models across diverse fields. Current research emphasizes improving existing bounds for algorithms in differential privacy, graph neural networks (particularly poly-GNNs), federated learning (both sequential and parallel), and optimization methods like proximal gradient descent, often analyzing convergence rates and error bounds. These advancements refine theoretical understanding and enable more accurate performance guarantees, impacting areas such as data privacy, machine learning model design, and algorithm verification.