Convex Optimization

Convex optimization is a powerful mathematical framework for finding the minimum or maximum of a convex function, with broad applications across science and engineering. Current research focuses on extending its capabilities to handle increasingly complex problems, including those involving non-Euclidean spaces, adversarial settings, bandit feedback, and high-dimensional data, often employing techniques like proximal methods, accelerated gradient descent, and distributed algorithms. These advancements are driving progress in diverse fields such as machine learning (e.g., training robust neural networks, federated learning), control theory (e.g., optimal control under uncertainty), and data privacy (e.g., differentially private optimization), leading to more efficient and effective solutions for real-world challenges.