Sampling Based Algorithm

Sampling-based algorithms are computational methods designed to efficiently approximate solutions to complex problems by strategically selecting a subset of data points for analysis. Current research focuses on improving the efficiency and accuracy of these algorithms across diverse applications, including machine learning (e.g., logistic regression, neural network training), robotics (motion planning), and game theory (multi-agent reinforcement learning), often employing techniques like principal component analysis, information-directed sampling, and Wasserstein proximals to enhance performance. These advancements are significant because they enable the analysis of large datasets and high-dimensional spaces that would be intractable with exhaustive methods, leading to improved model accuracy and faster computation in various scientific and engineering domains.