# Generalized Unrelated Machine Scheduling Problem

arXiv: https://arxiv.org/abs/2202.06292 · HTML: https://mlsanity.com/paper/2202.06292/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2022.

## Built on by (most cited first, 2 of 2)

- [Clustering to Minimize Cluster-Aware Norm Objectives](https://mlsanity.com/paper/2410.24104/) (2024)
- [A Broader View on Clustering under Cluster-Aware Norm Objectives](https://mlsanity.com/paper/2512.06211/) (2025)

## Abstract (arXiv)

We study the generalized load-balancing (GLB) problem, where we are given n jobs, each of which needs to be assigned to one of m unrelated machines with processing times pij. Under a job assignment σ, the load of each machine i is psii(pi[σ]) where psii:ℝnrightarrowℝ≥0 is a symmetric monotone norm and pi[σ] is the n-dimensional vector pij· 1[σ(j)=i]j∈ [n]. Our goal is to minimize the generalized makespan φ(mathsfload(σ)), where φ:ℝmrightarrowℝ≥0 is another symmetric monotone norm and mathsfload(σ) is the m-dimensional machine load vector. This problem significantly generalizes many classic optimization problems, e.g., makespan minimization, set cover, minimum-norm load-balancing, etc. We obtain a polynomial time randomized algorithm that achieves an approximation factor of O(log n), matching the lower bound of set cover up to constant factor. We achieve this by rounding a novel configuration LP relaxation with exponential number of variables. To approximately solve the configuration LP, we design an approximate separation oracle for its dual program. In particular, the separation oracle can be reduced to the norm minimization with a linear constraint (NormLin) problem and we devise a polynomial time approximation scheme (PTAS) for it, which may be of independent interest.