mlsanityData to 7 Oct 2026

Progress in Mathematical Programming Solvers from 2001 to 2020

arXiv 2206.09787 ↗First posted 20 Jun 2022math.OCcited by 10

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Abstract (arXiv)

This study investigates the progress made in LP and MILP solver performance during the last two decades by comparing the solver software from the beginning of the millennium with the codes available today. On average, we found out that for solving LP/MILP, computer hardware got about 20 times faster, and the algorithms improved by a factor of about nine for LP and around 50 for MILP, which gives a total speed-up of about 180 and 1,000 times, respectively. However, these numbers have a very high variance and they considerably underestimate the progress made on the algorithmic side: many problem instances can nowadays be solved within seconds, which the old codes are not able to solve within any reasonable time.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 1; 2026: 3.

10 papers.

  1. 2022Logic-Based Explainability in Machine Learningcited by 47
  2. 2024Algorithmic progress in language modelscited by 31
  3. 2023NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systemscited by 27
  4. 2022Algorithmic progress in computer visioncited by 13
  5. 2023Federated K-Means Clustering via Dual Decomposition-based Distributed Optimizationcited by 2
  6. 2025Bias Detection via Maximum Subgroup Discrepancycited by 2
  7. 2023Piecewise Polynomial Regression of Tame Functions via Integer Programmingcited by 1
  8. 2026Buffered AUC maximization for scoring systems via mixed-integer optimizationcited by 0
  9. 2026Intersectional Fairness via Mixed-Integer Optimizationcited by 0
  10. 2026Speeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branchingcited by 0