Local Search Proximal Algorithms as Decision Dynamics with Costs to Move

Acceptable moves for the “worthwhile-to-move” incremental principle are such that “advantages-to-move” are higher than some fraction of “costs-to-move”. When combined with optimization, this principle gives raise to adaptive local search proximal algorithms. Convergence results are given in two distinctive cases, namely low local costs-to-move and high local costs-to-move. In this last case, one obtains a dynamic cognitive approach to Ekeland’s ϵ-variational principle. Introduction of costs-to-move in the algorithms yields robustness and stability properties.