Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization

In this paper we study two inexact fast augmented Lagrangian algorithms for solving linearly constrained convex optimization problems. Our methods rely on a combination of the excessive-gap-like smoothing technique introduced in Nesterov (SIAM J Optim 16(1):235–249, 2005) and the general inexact oracle framework studied in Devolder (Math Program 146:37–75, 2014). We develop and analyze two augmented based algorithmic instances with constant and adaptive smoothness parameters, and derive a total computational complexity estimate in terms of projections on a simple primal feasible set for each algorithm. For the constant parameter algorithm we obtain the overall computational complexity of order (mathcal {O}(frac{1}{epsilon ^{5/4}})), while for the adaptive one we obtain (mathcal {O}(frac{1}{epsilon })) total number of projections onto the primal feasible set in order to achieve an (epsilon )-optimal solution for the original problem.