Mathematical Programming Computation, Volume 12, Issue 3, September 2020
Generation techniques for linear programming instances with controllable properties
Simon Bowly, Kate Smith-Miles, Davaatseren Baatar, Hans Mittelmann
This paper addresses the problem of generating synthetic test cases for experimentation in linear programming. We propose a method which maps instance generation and instance space search to an alternative encoded space. This allows us to develop a generator for feasible bounded linear programming instances with controllable properties. We show that this method is capable of generating any feasible bounded linear program, and that parameterised generators and search algorithms using this approach generate only feasible bounded instances. Our results demonstrate that controlled generation and instance space search using this method achieves feature diversity more effectively than using a direct representation.
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