Convex Quadratic

Convex quadratic programming focuses on efficiently solving optimization problems where the objective function is a convex quadratic and constraints are linear. Current research emphasizes developing and analyzing efficient algorithms, such as stochastic gradient descent (SGD) and its accelerated variants, along with specialized methods like vertex exchange and splitting techniques for handling various constraint structures, including those arising in applications like point set alignment and control barrier functions. These advancements improve the speed and scalability of solving large-scale problems, impacting diverse fields including machine learning, robotics, and operations research. The development of differentiable solvers further enhances their integration into larger optimization frameworks.