Learning Warm Starts for the Simplex Method from Measured Solver Cost
Read the original on arXiv Machine Learning →The paper introduces CostStart, a method that learns warm starts for the simplex algorithm by predicting which variables will be basic at optimality and then selecting the number of predicted columns, algorithm, and pricing rule based on measured instruction counts. Experiments on 25 classes of linear programs show that while a strong supervised predictor can reduce dual simplex iterations by two thirds, it only cuts solve time by one third; CostStart mitigates this gap and roughly halves solve time compared to the solver’s default start, outperforming two published methods on unseen problems up to four times larger.
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