Nearly-Linear Time LP Solvers and Rounding Algorithms for Scheduling Problems

Author Shi Li



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Shi Li
  • State Key Laboratory for Novel Software Technology, Nanjing University, China
  • https://tcs.nju.edu.cn/shili/
  • Department of Computer Science and Engineering, University at Buffalo, NY, USA

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Shi Li. Nearly-Linear Time LP Solvers and Rounding Algorithms for Scheduling Problems. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 86:1-86:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICALP.2023.86

Abstract

We study nearly-linear time approximation algorithms for non-preemptive scheduling problems in two settings: the unrelated machine setting, and the identical machine with job precedence constraints setting, under the well-studied objectives such as makespan and weighted completion time. For many problems, we develop nearly-linear time approximation algorithms with approximation ratios matching the current best ones achieved in polynomial time. Our main technique is linear programming relaxation. For the unrelated machine setting, we formulate mixed packing and covering LP relaxations of nearly-linear size, and solve them approximately using the nearly-linear time solver of Young. For the makespan objective, we develop a rounding algorithm with (2+ε)-approximation ratio. For the weighted completion time objective, we prove the LP is as strong as the rectangle LP used by Im and Li, leading to a nearly-linear time (1.45 + ε)-approximation for the problem. For problems in the identical machine with precedence constraints setting, the precedence constraints can not be formulated as packing or covering constraints. To achieve the nearly-linear running time, we define a polytope for the constraints, and leverage the multiplicative weight update (MWU) method with an oracle which always returns solutions in the polytope.

Subject Classification

ACM Subject Classification
  • Theory of computation → Scheduling algorithms
Keywords
  • Nearly-Linear Time
  • Sheduling
  • Approximation Algorithms

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References

  1. Zeyuan Allen-Zhu and Lorenzo Orecchia. Nearly-linear time positive LP solver with faster convergence rate. In Proceedings of the Forty-Seventh Annual ACM Symposium on Theory of Computing (STOC 2015), pages 229-236, 2015. Google Scholar
  2. Måns Alskog. Implementation of a fast approximation algorithm for precedence constrained scheduling. Master’s thesis, Linköping University, Applied Mathematics, Faculty of Science and Engineering, 2022. Google Scholar
  3. Sanjeev Arora, Elad Hazan, and Satyen Kale. The multiplicative weights update method: a meta-algorithm and applications. Theory of Computing, 8(6):121-164, 2012. URL: https://doi.org/10.4086/toc.2012.v008a006.
  4. Nikhil Bansal and Subhash Khot. Optimal long code test with one free bit. In Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2009), pages 453-462, 2009. Google Scholar
  5. Nikhil Bansal, Aravind Srinivasan, and Ola Svensson. Lift-and-round to improve weighted completion time on unrelated machines. In Proceedings of the Forty-eighth Annual ACM Symposium on Theory of Computing (STOC 2016), pages 156-167, 2016. Google Scholar
  6. Yair Bartal and Lee-Ad Gottlieb. Near-linear time approximation schemes for steiner tree and forest in low-dimensional spaces. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2021), pages 1028-1041, 2021. URL: https://doi.org/10.1145/3406325.3451063.
  7. A. Bernstein, M. Gutenberg, and T. Saranurak. Deterministic decremental sssp and approximate min-cost flow in almost-linear time. In Proceedings of the 62nd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2021), pages 1000-1008, 2021. URL: https://doi.org/10.1109/FOCS52979.2021.00100.
  8. Jan van den Brand, Yin Tat Lee, Aaron Sidford, and Zhao Song. Solving tall dense linear programs in nearly linear time. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2020), pages 775-788, 2020. URL: https://doi.org/10.1145/3357713.3384309.
  9. Chandra Chekuri, Sariel Har-Peled, and Kent Quanrud. Fast lp-based approximations for geometric packing and covering problems. In Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2020), pages 1019-1038, 2020. Google Scholar
  10. Chandra Chekuri, T.S. Jayram, and Jan Vondrak. On multiplicative weight updates for concave and submodular function maximization. In Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science (ITCS 2015), pages 201-210, 2015. Google Scholar
  11. Chandra Chekuri and Kent Quanrud. Near-linear time approximation schemes for some implicit fractional packing problems. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2017), pages 801-820, 2017. Google Scholar
  12. Chandra Chekuri and Kent Quanrud. Fast approximations for metric-tsp via linear programming. arXiv, abs/1802.01242, 2018. URL: https://arxiv.org/abs/1802.01242.
  13. Chandra Chekuri and Kent Quanrud. Randomized MWU for positive LPs. In Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2018), pages 358-377, 2018. Google Scholar
  14. Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, and Sushant Sachdeva. Maximum flow and minimum-cost flow in almost-linear time. In Proceedings of the 63rd Annual IEEE Symposium on Foundations of Computer Science (FOCS 2022), pages 612-623, 2022. URL: https://doi.org/10.1109/FOCS54457.2022.00064.
  15. Julia Chuzhoy, Yu Gao, Jason Li, Danupon Nanongkai, Richard Peng, and Thatchaphol Saranurak. A deterministic algorithm for balanced cut with applications to dynamic connectivity, flows, and beyond. In Sandy Irani, editor, Proceedings of the 61st Annual IEEE Annual Symposium on Foundations of Computer Science (FOCS 2020), pages 1158-1167, 2020. URL: https://doi.org/10.1109/FOCS46700.2020.00111.
  16. Naveen Garg and Jochen Könemann. Faster and simpler algorithms for multicommodity flow and other fractional packing problems. SIAM Journal on Computing, 37(2):630-652, 2007. URL: https://doi.org/10.1137/S0097539704446232.
  17. Shashwat Garg. Quasi-PTAS for scheduling with precedences using LP hierarchies. In Proceedings of 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018), pages 59:1-59:13, 2018. Google Scholar
  18. R. L. Graham. Bounds on multiprocessing timing anomalies. Siam Journal on Applied Mathematics, 17(2):416-429, 1969. Google Scholar
  19. R. L. Graham, E. L. Lawler, J. K. Lenstra, and A. H. G. Rinnooy Kan. Optimization and approximation in deterministic sequencing and scheduling: a survey. Ann. Discrete Math., 4:287-326, 1979. Google Scholar
  20. Leslie A. Hall, Andreas S. Schulz, David B. Shmoys, and Joel Wein. Scheduling to minimize average completion time: Off-line and on-line approximation algorithms. Math. Oper. Res., 22(3):513-544, August 1997. Google Scholar
  21. John E. Hopcroft and Richard M. Karp. An n^5/2 algorithm for maximum matchings in bipartite graphs. SIAM Journal on Computing, 2(4):225-231, 1973. URL: https://doi.org/10.1137/0202019.
  22. Sungjin Im and Shi Li. Improved approximations for unrelated machine scheduling. In Proceedings of the Thirty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2023), pages 2917-2946, 2023. URL: https://doi.org/10.1137/1.9781611977554.ch111.
  23. Sungjin Im and Maryam Shadloo. Weighted completion time minimization for unrelated machines via iterative fair contention resolution [extended abstract]. In Proceedings of the Thirty-First Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2020), pages 2790-2809, 2020. Google Scholar
  24. Klaus Jansen and Lars Rohwedder. On the configuration-LP of the restricted assignment problem. In Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2017), pages 2670-2678, 2017. Google Scholar
  25. Klaus Jansen and Lars Rohwedder. A quasi-polynomial approximation for the restricted assignment problem. SIAM Journal on Computing, 49(6):1083-1108, 2020. Google Scholar
  26. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford. An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2014), pages 217-226, 2014. Google Scholar
  27. Christos Koufogiannakis and N. Young. A nearly linear-time ptas for explicit fractional packing and covering linear programs. Algorithmica, 70:648-674, 2013. Google Scholar
  28. Christos Koufogiannakis and Neal E. Young. Beating simplex for fractional packing and covering linear programs. In 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2007), pages 494-504, 2007. URL: https://doi.org/10.1109/FOCS.2007.62.
  29. Yin Tat Lee and Aaron Sidford. Efficient inverse maintenance and faster algorithms for linear programming. In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS 2015), pages 230-249, 2015. Google Scholar
  30. Yin Tat Lee, Zhao Song, and Qiuyi Zhang. Solving empirical risk minimization in the current matrix multiplication time. In Proceedings of the Thirty-Second Conference on Learning Theory (COLT 2019), pages 2140-2157, 2019. Google Scholar
  31. J. K. Lenstra and A. H. G. Rinnooy Kan. Complexity of scheduling under precedence constraints. Oper. Res., 26(1):22-35, 1978. Google Scholar
  32. Jan Karel Lenstra, David B. Shmoys, and Éva Tardos. Approximation algorithms for scheduling unrelated parallel machines. Mathematical Programming, 46:259-271, 1990. Google Scholar
  33. Elaine Levey and Thomas Rothvo. A (1+ε)-approximation for makespan scheduling with precedence constraints using lp hierarchies. SIAM Journal on Computing, 50(3):STOC16-201-STOC16-217, 2021. Google Scholar
  34. Jason Li. Deterministic mincut in almost-linear time. In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2021), pages 384-395, 2021. URL: https://doi.org/10.1145/3406325.3451114.
  35. Shi Li. Scheduling to minimize total weighted completion time via time-indexed linear programming relaxations. SIAM Journal on Computing, 49(4):FOCS17-409-FOCS17-440, 2020. Google Scholar
  36. Michael Luby and Noam Nisan. A parallel approximation algorithm for positive linear programming. In Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing (STOC 1993), pages 448-457, 1993. URL: https://doi.org/10.1145/167088.167211.
  37. Alix Munier, Maurice Queyranne, and Andreas S. Schulz. Approximation bounds for a general class of precedence constrained parallel machine scheduling problems. In Integer Programming and Combinatorial Optimization (IPCO 1998), pages 367-382, 1998. Google Scholar
  38. Richard Peng. Approximate undirected maximum flows in O(m polylog(n)) time. In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2016), pages 1862-1867, 2016. Google Scholar
  39. Serge A. Plotkin, David B. Shmoys, and Éva Tardos. Fast approximation algorithms for fractional packing and covering problems. Mathematics of Operations Research, 20(2):257-301, 1995. URL: https://doi.org/10.1287/moor.20.2.257.
  40. Paul Purdom. A transitive closure algorithm. BIT Numerical Mathematics, 10:76-94, 1970. Google Scholar
  41. Maurice Queyranne and Andreas S. Schulz. Approximation bounds for a general class of precedence constrained parallel machine scheduling problems. SIAM J. Comput., 35(5):1241-1253, May 2006. Google Scholar
  42. Andreas S. Schulz and Martin Skutella. Scheduling unrelated machines by randomized rounding. SIAM J. Discret. Math., 15(4):450-469, April 2002. Google Scholar
  43. Jay Sethuraman and Mark S. Squillante. Optimal scheduling of multiclass parallel machines. In Proceedings of the Tenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 1999), pages 963-964, 1999. Google Scholar
  44. Farhad Shahrokhi and D. W. Matula. The maximum concurrent flow problem. J. ACM, 37(2):318-334, April 1990. URL: https://doi.org/10.1145/77600.77620.
  45. Jonah Sherman. Area-convexity, 𝓁_∞ regularization, and undirected multicommodity flow. In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (STOC 2017), pages 452-460, 2017. URL: https://doi.org/10.1145/3055399.3055501.
  46. David B. Shmoys and Éva Tardos. An approximation algorithm for the generalized assignment problem. Math. Program., 62(1–3):461-474, February 1993. Google Scholar
  47. Martin Skutella. Convex quadratic and semidefinite programming relaxations in scheduling. J. ACM, 48(2):206-242, March 2001. Google Scholar
  48. Ola Svensson. Conditional hardness of precedence constrained scheduling on identical machines. In Proceedings of the Forty-second ACM Symposium on Theory of Computing (STOC 2010), pages 745-754, 2010. Google Scholar
  49. Ola Svensson. Santa Claus schedules jobs on unrelated machines. SIAM Journal on Computing, 41(5):1318-1341, 2012. Google Scholar
  50. Neal E. Young. Randomized rounding without solving the linear program. In Proceedings of the Sixth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 1995), pages 170-178, 1995. Google Scholar
  51. Neal E. Young. Nearly linear-work algorithms for mixed packing/covering and facility-location linear programs, 2014. URL: https://arxiv.org/abs/1407.3015.
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