Controlled rounding for tables with subtotals
Annals of Operations Research
Primal-dual interior-point methods
Primal-dual interior-point methods
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Network Flows Heuristics for Complementary Cell Suppression: An Empirical Evaluation and Extensions
Inference Control in Statistical Databases, From Theory to Practice
Inference Control in Statistical Databases, From Theory to Practice
Solving Real-World Linear Programs: A Decade and More of Progress
Operations Research
Solving the Cell Suppression Problem on Tabular Data with Linear Constraints
Management Science
An interior-point approach for primal block-angular problems
Computational Optimization and Applications
A Shortest-Paths Heuristic for Statistical Data Protection in Positive Tables
INFORMS Journal on Computing
A heuristic block coordinate descent approach for controlled tabular adjustment
Computers and Operations Research
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Methods for the protection of statistical tabular data—as controlled tabular adjustment, cell suppression, or controlled rounding—need to solve several linear programming subproblems. For large multidimensional linked and hierarchical tables, such subproblems turn out to be computationally challenging. One of the techniques used to reduce the solution time of mathematical programming problems is to exploit the constraints structure using some specialized algorithm. Two of the most usual structures are block-angular matrices with either linking rows (primal block-angular structure) or linking columns (dual block-angular structure). Although constraints associated to tabular data have intrinsically a lot of structure, current software for tabular data protection neither detail nor exploit it, and simply provide a single matrix, or at most a set of smallest submatrices. We provide in this work an efficient tool for the automatic detection of primal or dual block-angular structure in constraints matrices. We test it on some of the complex CSPLIB instances, showing that when the number of linking rows or columns is small, the computational savings are significant.