Efficient Optimization
Efficient optimization seeks to find the best solution to a problem quickly and with minimal resources, a crucial goal across diverse scientific and engineering domains. Current research focuses on developing and refining algorithms like modified rat swarm optimizers, Bayesian optimization methods, and various neural network architectures tailored to specific problem types (e.g., object detection, reinforcement learning). These advancements improve the speed and accuracy of optimization in applications ranging from medical image analysis and power grid management to drug discovery and large language model training, ultimately impacting the efficiency and effectiveness of numerous fields.
Papers
Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
Oliver Anton, Victoria A. Henderson, Elisa Da Ros, Ivan Sekulic, Sven Burger, Philipp-Immanuel Schneider, Markus Krutzik
Optimizing Distributed Training on Frontier for Large Language Models
Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash