  $\Gamma$-maximin, $\Gamma$-maximax and inteval dominance are familiar
decision criteria for making decisions under severe uncertainty, when
probability distributions can only be partially identified. One can apply these
three criteria by solving sequences of linear programs. In this study, we
present new algorithms for these criteria and compare their performance to
existing standard algorithms. Specifically, we use efficient ways, based on
previous work, to find common initial feasible points for these algorithms.
Exploiting these initial feasible points, we develop early stopping criteria to
determine whether gambles are either $\Gamma$-maximin, $\Gamma$-maximax or
interval dominant. We observe that the primal-dual interior point method
benefits considerably from these improvements. In our simulation, we find that
our proposed algorithms outperform the standard algorithms when the size of the
domain of lower previsions is less or equal to the sizes of decisions and
outcomes. However, our proposed algorithms do not outperform the standard
algorithms in the case that the size of the domain of lower previsions is much
larger than the sizes of decisions and outcomes.
