A re-scaled twin augmented Lagrangian algorithm for saddle point seeking

In [A. Ouorou, A primal-dual algorithm for monotropic programming and its application to network optimization, Computational Optimization and Application 15 (2002) 125–143], a block-wise Gauss–Seidel method has been developed for monotropic programming problems, using two different quadratic augmented Lagrangian functions defined for the primal and the dual problems. In this paper, we extend the concept by introducing a nonlinear re-scaling principle obtained recently by Polyak [R. Polyak, Nonlinear rescaling vs smoothing technique in constrained optimization, Mathematical Programming 92 (2002) 197–235].