New algorithm uses learning to improve scheduling approximation ratios

By PulseAugur Editorial · [2 sources] · 2026-06-11 09:55

Researchers have developed a new learning-augmented algorithm for makespan minimization on unrelated machines, a problem denoted as R||Cmax. This approach extends a framework previously used for selection problems to scheduling, aiming to improve approximation ratios by incorporating predictions of job assignments. The algorithm achieves a (1+ε)-approximation for accurate predictions, with the approximation degrading to a 2-approximation as prediction error increases. AI

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New algorithm uses learning to improve scheduling approximation ratios

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arXiv cs.LG TIER_1 English(EN) · Giorgos Mitropoulos · 2026-06-11 09:55

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly matches theoretical lower bounds, making its generalization highly compelling. We address an open qu…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-11 09:55

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems. Despite its simplicity, this approach tightly matches theoretical lower bounds, making its generalization highly compelling. We address an open qu…

COVERAGE [2]

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

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