{'fraction_non_alphanumeric': 0.025165562913907286, 'fraction_numerical': 0.017218543046357615, 'mean_word_length': 5.24793388429752, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Interval Branch-and-Prune Algorithm for Discrete Minimax Problems

We present an interval branch-and-prune method for a discrete minimax problem where the constituent minimax functions are continuously difierentiable functions of one real variable. Our approach is based on smoothing the max-type function by exploiting the Jaynes’s maximum entropy [Phys. Rev., 106:620{630, 1957]. Our algorithm works within the branch and bound framework and incorporates a new accelerating device that composes inner and outer pruning steps. Pruning is achieved by using flrst order information of the entropic objective function by means of an interval evaluation. Our algorithm was implemented and tested on a complete test set and numerical results are presented.