Proximal bundle methods based on approximate subgradients for solving Lagrangian duals of minimax fractional programs

Abstract

We are interested in this work to solve the Lagrangian dual of a generalized fractional program (GFP), which gives the minimal value of the primal problem, thanks to some duality results. With the help of a general minimax equality assumption, we give duality results under minimal assumptions. Since the associated parametric programs of the dual of GFP are always concave, we use a general approximating proximal scheme to these subproblems and construct bundle methods by means of approximate values and approximate subgradients of the dual parametric function. As for all dual algorithms, the proposed methods generate a sequence of values that converges from below to the minimal value of the GFP and a sequence of approximate solutions that converges to a solution of the Lagrangian dual of the GFP. For certain classes of problems, the convergence is at least linear.