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Christoph Neumann (christoph.neumannkit.edu) Abstract: A feasible rounding approach is a novel technique to compute good feasible points for mixedinteger optimization problems. The central idea of this approach is the construction of a continuously described inner parallel set for which any rounding of any of its elements is feasible in the original mixedinteger problem. It is known that this approach is promising for problems in which no equality constraints on integer variables appear. Yet, so far the potential of incorporating equality constraints with integer variables into this approach remained unclear. In this article we close this gap by developing a reduction scheme that enables the application of feasible rounding approaches to problems in which such equality constraints occur. Our computational study on a large test bed of MIPLIB instances shows that this reduction is applicable to a relevant share of practical problems. Moreover, our results illustrate that a nonempty inner parallel set is possible, but less likely to occur for practical problems with equality constraints on integer variables (compared to problems without such constraints). Finally, our results indicate that the application of a feasible rounding approach can be beneficial for the computation of good feasible points even under the occurrence of equality constraints on integer variables. Keywords: Granularity, Inner parallel set, Reduction scheme, Hermite normal form, Feasible point Category 1: Integer Programming ((Mixed) Integer Linear Programming ) Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming ) Citation: Optimization Online, Preprint ID 2020108045, 2020 Download: [PDF] Entry Submitted: 10/01/2020 Modify/Update this entry  
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