A relax-and-cut temporal decomposition with partially relaxed look-ahead windows and dynamic N-1 cut separation solves SCUC faster than monolithic Gurobi while keeping primal gaps near 1%.
Efficient primal heuristics for mixed-integer linear programs
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abstract
This paper is a short report about our work for the primal task in the Machine Learning for Combinatorial Optimization NeurIPS 2021 Competition. For each dataset of our interest in the competition, we propose customized primal heuristic methods to efficiently identify high-quality feasible solutions. The computational studies demonstrate the superiority of our proposed approaches over the competitors'.
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Relax-and-Cut for Temporal SCUC Decomposition
A relax-and-cut temporal decomposition with partially relaxed look-ahead windows and dynamic N-1 cut separation solves SCUC faster than monolithic Gurobi while keeping primal gaps near 1%.