Numerical computation of entropy-regularized quadratic optimization problems

. Entropy-regularized quadratic optimization problems are a special class of optimization problems with wide applications in various fields, such as transportation and machine learning. In this paper, we apply the augmented Lagrangian method to this problem with its subproblem solved by the block coordinate descent method. Under certain mild conditions, we analyze the global convergence of this algorithm. Numerical experiments demonstrate the effectiveness of this algorithm.