Circulant Matrix

Circulant matrices, characterized by their circularly shifted rows, are finding increasing application across diverse fields. Current research focuses on leveraging their structure for computational efficiency in areas like deep learning inference (through techniques like block circulant transformations) and optimization problems solved by Ising machines. This focus stems from the ability of circulant structures to reduce computational complexity, improve training stability in neural networks, and enhance the performance of algorithms for tasks such as similarity estimation and speaker verification. The resulting improvements in speed and accuracy have significant implications for both theoretical advancements and practical applications in machine learning and beyond.