A fix-and-relax heuristic for controlled tabular adjustment

Controlled tabular adjustment (CTA) is an emerging protect ion technique for tabular data pro- tection. CTA formulates a mixed integer linear programming problem, which is tough for tables of moderate size. Finding a feasible initial solution may ev en be a challenging task for large instances. On the other hand, end users of tabular data prote ction techniques give priority to fast executions and are thus satisfied in practice with suboptima l solutions. In this work the fix-and- relax strategy is applied to large CTA instances. Fix-and-r elax is based on partitioning the set of binary variables into clusters to selectively explore a sma ller branch-and-cut tree. We report ex- tensive computational results on a set of real and random CTA instances. Fix-and-relax is shown to be competitive compared to plain CPLEX branch-and-cut in terms of quickly finding either a feasible solution or a good upper bound in di ffi cult instances