Computational Techniques for Investigating Information Theoretic Limits of Information Systems

Computer-aided methods, based on the entropic linear program framework, have been shown to be effective in assisting study of information theoretic fundamental limits systems. One key element that significantly impacts their computation efficiency and applicability is reduction variables, problem-specific symmetry dependence relations. In this work, we propose using disjoint-set data structure algorithmically identify mapping, instead relying exhaustive enumeration equivalence classification. Based reduced program, consider four techniques investigate systems: (1) computing an outer bound for a given combination measures providing values at optimal solution; (2) efficiently polytope tradeoff between two quantities; (3) producing proof (as weighted sum known inequalities) computed bound; (4) range quantities which value does not change, i.e., sensitivity analysis. A toolbox, with efficient JSON format input frontend, either Gurobi or Cplex as solving engine, implemented open-sourced.