Chordal Sparsity

Chordal sparsity leverages the structure of sparse matrices to significantly speed up computations in various optimization problems, particularly those involving semidefinite programming (SDP). Current research focuses on applying this technique to improve the efficiency of algorithms in diverse fields, including robotics, computer vision (e.g., rotation averaging and principal component analysis), and machine learning (e.g., neural network verification and feature selection). This enhanced efficiency allows for the solution of larger and more complex problems, leading to improved accuracy and scalability in applications ranging from robust estimation to model verification.