  In this paper, we are motivated by two important applications:
entropy-regularized optimal transport problem and road or IP traffic demand
matrix estimation by entropy model. Both of them include solving a special type
of optimization problem with linear equality constraints and objective given as
a sum of an entropy regularizer and a linear function. It is known that the
state-of-the-art solvers for this problem, which are based on Sinkhorn's method
(also known as RSA or balancing method), can fail to work, when the
entropy-regularization parameter is small. We consider the above optimization
problem as a particular instance of a general strongly convex optimization
problem with linear constraints. We propose a new algorithm to solve this
general class of problems. Our approach is based on the transition to the dual
problem. First, we introduce a new accelerated gradient method with adaptive
choice of gradient's Lipschitz constant. Then, we apply this method to the dual
problem and show, how to reconstruct an approximate solution to the primal
problem with provable convergence rate. We prove the rate $O(1/k^2)$, $k$ being
the iteration counter, both for the absolute value of the primal objective
residual and constraints infeasibility. Our method has similar to Sinkhorn's
method complexity of each iteration, but is faster and more stable numerically,
when the regularization parameter is small. We illustrate the advantage of our
method by numerical experiments for the two mentioned applications. We show
that there exists a threshold, such that, when the regularization parameter is
smaller than this threshold, our method outperforms the Sinkhorn's method in
terms of computation time.
