Global Optimization
Global optimization aims to find the absolute best solution within a vast search space, a crucial task across numerous scientific and engineering disciplines. Current research emphasizes developing efficient algorithms that overcome challenges like high dimensionality, non-convexity, and the presence of numerous local optima, focusing on methods such as Bayesian optimization, evolutionary algorithms (including modifications like MRSO), and novel approaches leveraging generative models and large language models to guide the search. These advancements improve the accuracy and speed of finding optimal solutions for complex problems, impacting fields ranging from robotics and materials science to machine learning and engineering design.
Papers
A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation
Georgy Ponimatkin, Nermin Samet, Yang Xiao, Yuming Du, Renaud Marlet, Vincent Lepetit
Global Optimization for Cardinality-constrained Minimum Sum-of-Squares Clustering via Semidefinite Programming
Veronica Piccialli, Antonio M. Sudoso
Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning
Pablo G. Morato, Charalampos P. Andriotis, Konstantinos G. Papakonstantinou, Philippe Rigo
Optimistic Optimization of Gaussian Process Samples
Julia Grosse, Cheng Zhang, Philipp Hennig