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Using Linear Programming to Find Approximate Solutions to the Fields to Impute Problem for Industry Data

In: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface

Author

Listed:
  • Patrick G. McKeown

    (University of Georgia)

  • Joanne R. Schaffer

    (University of Georgia)

Abstract

Sande has suggested a mathematical programming formulation of the fields to impute problem (FTIP) for continuous data. This formulation seeks to find a minimum weighted sum of fields that would need to be changed to yield an acceptable record by solving a mixed integer programming problem known as the fixed charge problem. While this formulation can and has been solved to find an optimal solution to the FTIP, this approach can be expensive in terms of solution time. In this paper, we demonstrate the use of a heuristic procedure to find an approximately optimal solution to FTIP. This procedure uses the SWIFT algorithm developed by Walker in conjunction with a judicious choice of dummy variable costs to arrive at an approximate solution based on a linear programming solution. We will show that this solution is optimal in many cases. We will also discuss the use of the special structure of FTIP to arrive at an optimal solution to the LP problem.

Suggested Citation

  • Patrick G. McKeown & Joanne R. Schaffer, 1981. "Using Linear Programming to Find Approximate Solutions to the Fields to Impute Problem for Industry Data," Springer Books, in: William F. Eddy (ed.), Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, pages 288-291, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4613-9464-8_41
    DOI: 10.1007/978-1-4613-9464-8_41
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