Optimization Purpose
Optimization, the process of finding the best solution from a set of possibilities, is a fundamental problem across numerous scientific and engineering disciplines. Current research focuses on improving the efficiency and robustness of optimization algorithms, particularly for complex, high-dimensional problems, employing techniques like graph neural networks, normalizing flows, and variants of gradient descent tailored to specific architectures (e.g., transformers, neural differential equations). These advancements are crucial for addressing challenges in diverse fields, ranging from power systems management and robotic control to machine learning model training and resource allocation, ultimately leading to more efficient and effective solutions in various applications.
Papers - Page 5
A Survey of Reinforcement Learning for Optimization in Automation
Ahmad Farooq, Kamran IqbalPredicting Drive Test Results in Mobile Networks Using Optimization Techniques
MohammadJava Taheri, Abolfazl Diyanat, MortezaAli Ahmadi, Ali NazariA Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian Optimization and Bidirectional Recurrent Unit
Tianyi Huang, Zeqiu Xu, Peiyang Yu, Jingyuan Yi, Xiaochuan Xu
User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization
Joshua Hang Sai Ip, Ankush Chakrabarty, Ali Mesbah, Diego RomeresProperties of Wasserstein Gradient Flows for the Sliced-Wasserstein Distance
Christophe Vauthier, Quentin Mérigot, Anna KorbaImproved Extrinsic Calibration of Acoustic Cameras via Batch Optimization
Zhi Li, Jiang Wang, Xiaoyang Li, He Kong
Optimization under Attack: Resilience, Vulnerability, and the Path to Collapse
Amal Aldawsari, Evangelos PournarasI3S: Importance Sampling Subspace Selection for Low-Rank Optimization in LLM Pretraining
Haochen Zhang, Junze Yin, Guanchu Wang, Zirui Liu, Tianyi Zhang, Anshumali Shrivastava, Lin Yang, Vladimir Braverman
Generative-enhanced optimization for knapsack problems: an industry-relevant study
Yelyzaveta Vodovozova, Abhishek Awasthi, Caitlin Jones, Joseph Doetsch, Karen Wintersperger, Florian Krellner, Carlos A. RiofríoCoherent Local Explanations for Mathematical Optimization
Daan Otto, Jannis Kurtz, S. Ilker Birbil
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
Yinjie Wang, Ling Yang, Guohao Li, Mengdi Wang, Bryon AragamEnsuring Reliability via Hyperparameter Selection: Review and Advances
Amirmohammad Farzaneh, Osvaldo SimeoneEfficient Distributed Optimization under Heavy-Tailed Noise
Su Hyeong Lee, Manzil Zaheer, Tian LiPINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal Transport
Di Wu, Ling Liang, Haizhao Yang
Honegumi: An Interface for Accelerating the Adoption of Bayesian Optimization in the Experimental Sciences
Sterling G. Baird, Andrew R. Falkowski, Taylor D. SparksAre Language Models Up to Sequential Optimization Problems? From Evaluation to a Hegelian-Inspired Enhancement
Soheil AbbaslooAdaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments
Hyeonho Noh, Byonghyo Shim, Hyun Jong Yang