The fields of optimal transport (OT) and optimization have a rich and intertwined history, providing foundational tools for mathematics, statistics, and computer science. These disciplines overlap with a broad range of topics, including partial differential equations (PDEs), inverse problems, and machine learning. The deep theoretical foundations of both fields are critical for addressing a wide range of classical and modern computational challenges.
This workshop aims to bring together researchers from three diverse communities—optimal transport, optimization, and operations research—to discuss latest theoretical advancements and foster interdisciplinary dialogue and collaboration.
The workshop will explore intricate connections of these fields in cutting-edge applications such as deep generative models, domain adaptation, robust optimization, and econ theory,
