Scheduling Lower Bounds via AND Subset Sum

Authors Amir Abboud, Karl Bringmann, Danny Hermelin, Dvir Shabtay



PDF
Thumbnail PDF

File

LIPIcs.ICALP.2020.4.pdf
  • Filesize: 0.52 MB
  • 15 pages

Document Identifiers

Author Details

Amir Abboud
  • IBM Almaden Research Center, San Jose, CA, USA
Karl Bringmann
  • Saarland University, Saarland Informatics Campus (SIC), Saarbrücken, Germany
  • Max Planck Institute for Informatics, Saarland Informatics Campus (SIC), Saarbrücken, Germany
Danny Hermelin
  • Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beersheba, Israel
Dvir Shabtay
  • Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beersheba, Israel

Cite As Get BibTex

Amir Abboud, Karl Bringmann, Danny Hermelin, and Dvir Shabtay. Scheduling Lower Bounds via AND Subset Sum. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 4:1-4:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ICALP.2020.4

Abstract

Given N instances (X_1,t_1),…,(X_N,t_N) of Subset Sum, the AND Subset Sum problem asks to determine whether all of these instances are yes-instances; that is, whether each set of integers X_i has a subset that sums up to the target integer t_i. We prove that this problem cannot be solved in time Õ((N ⋅ t_max)^{1-ε}), for t_max = max_i t_i and any ε > 0, assuming the ∀ ∃ Strong Exponential Time Hypothesis (∀∃-SETH). We then use this result to exclude Õ(n+P_max⋅n^{1-ε})-time algorithms for several scheduling problems on n jobs with maximum processing time P_max, assuming ∀∃-SETH. These include classical problems such as 1||∑ w_jU_j, the problem of minimizing the total weight of tardy jobs on a single machine, and P₂||∑ U_j, the problem of minimizing the number of tardy jobs on two identical parallel machines.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • SETH
  • fine grained complexity
  • Subset Sum
  • scheduling

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Amir Abboud, Karl Bringmann, Danny Hermelin, and Dvir Shabtay. SETH-based lower bounds for subset sum and bicriteria path. Proc. of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 41-57, 2019. Google Scholar
  2. Amir Abboud, Virginia Vassilevska Williams, and Joshua Wang. Approximation and fixed parameter subquadratic algorithms for radius and diameter in sparse graphs. In Proc. of the 27th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 377-391. SIAM, 2016. Google Scholar
  3. Bertie Ancona, Monika Henzinger, Liam Roditty, Virginia Vassilevska Williams, and Nicole Wein. Algorithms and hardness for diameter in dynamic graphs. In Proc. of the 46th International Colloquium on Automata, Languages, and Programming (ICALP), volume 132 of LIPIcs, pages 13:1-13:14, 2019. Google Scholar
  4. Kyriakos Axiotis, Arturs Backurs, Ce Jin, Christos Tzamos, and Hongxun Wu. Fast modular subset sum using linear sketching. In Proc. of the 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 58-69. SIAM, 2019. Google Scholar
  5. Felix A. Behrend. On sets of integers which contain no three terms in arithmetical progression. Proc. of the National Academy of Sciences, 32(12):331-332, 1946. Google Scholar
  6. Richard E. Bellman. Dynamic programming. Princeton University Press, 1957. Google Scholar
  7. Karl Bringmann. A near-linear pseudopolynomial time algorithm for subset sum. In Proc. of of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1073-1084, 2017. Google Scholar
  8. Karl Bringmann and Bhaskar Ray Chaudhury. Polyline simplification has cubic complexity. In Proc. of the 35th International Symposium on Computational Geometry (SoCG), pages 18:1-18:16, 2019. Google Scholar
  9. Peter Brucker. Scheduling Algorithms. Springer, 2006. Google Scholar
  10. Marco L. Carmosino, Jiawei Gao, Russell Impagliazzo, Ivan Mihajlin, Ramamohan Paturi, and Stefan Schneider. Nondeterministic extensions of the Strong Exponential Time Hypothesis and consequences for non-reducibility. In Proc. of the 7th ACM Conference on Innovations in Theoretical Computer Science (ITCS), pages 261-270, 2016. Google Scholar
  11. T.C. Edwin Cheng, Yakov M. Shafransky, and Chi To Ng. An alternative approach for proving the NP-hardness of optimization problems. European Journal of Operational Research, 248(1):52-58, 2016. Google Scholar
  12. Marek Cygan, Holger Dell, Daniel Lokshtanov, Dániel Marx, Jesper Nederlof, Yoshio Okamoto, Ramamohan Paturi, Saket Saurabh, and Magnus Wahlström. On problems as hard as CNF-SAT. ACM Transactions on Algorithms, 12(3):41, 2016. Google Scholar
  13. Erik D. Demaine, Andrea Lincoln, Quanquan C. Liu, Jayson Lynch, and Virginia Vassilevska Williams. Fine-grained I/O complexity via reductions: New lower bounds, faster algorithms, and a time hierarchy. In Proc. of the 9th Innovations in Theoretical Computer Science Conference (ITCS), volume 94 of LIPIcs, pages 34:1-34:23, 2018. Google Scholar
  14. Danny Dolev and Manfred K. Warmuth. Profile scheduling of opposing forests and level orders. SIAM Journal on Algebraic and Discrete Methods, 6(4):665-687, 1985. Google Scholar
  15. Jianzhong Du and Joseph Y.-T. Leung. Minimizing total tardiness on one machine is NP-hard. Mathematics of Operations Research, 15(3):483-495, 1990. Google Scholar
  16. Friedrich Eisenbrand and Robert Weismantel. Proximity results and faster algorithms for integer programming using the Steinitz lemma. In Proc. of the 29th Annual ACM-SIAM Symposium On Discrete Algorithms (SODA), pages 808-816, 2018. Google Scholar
  17. Zvi Galil and Oded Margalit. An almost linear-time algorithm for the dense subset-sum problem. SIAM Journal on Computing, 20(6):1157-1189, 1991. Google Scholar
  18. Jiawei Gao, Russell Impagliazzo, Antonina Kolokolova, and Ryan Williams. Completeness for first-order properties on sparse structures with algorithmic applications. ACM Transactions on Algorithms, 15(2):1-35, 2018. Google Scholar
  19. Rosario Giuseppe Garroppo, Stefano Giordano, and Luca Tavanti. A survey on multi-constrained optimal path computation: Exact and approximate algorithms. Computer Networks, 54(17):3081-3107, 2010. Google Scholar
  20. Ronald Graham, Eugene Lawler, Jan K. Lenstra, and Alexander R. Kan. Optimization and approximation in deterministic sequencing and scheduling: A survey. Annals of Discrete Mathematics, 5:287-326, 1979. Google Scholar
  21. Russell Impagliazzo and Ramamohan Paturi. On the complexity of k-SAT. Journal of Computer and System Sciences, 62(2):367-375, 2001. Google Scholar
  22. Russell Impagliazzo, Ramamohan Paturi, and Francis Zane. Which problems have strongly exponential complexity? Journal of Computer and System Sciences, 63(4):512-530, 2001. Google Scholar
  23. Ce Jin and Hongxun Wun. A simple near-linear pseudopolynomial time randomized algorithm for subset sum. In Proc. of the 2nd Symposium On Simplicity in Algorithms (SOSA), pages 17:1-17:6, 2018. Google Scholar
  24. Richard M. Karp. Reducibility among combinatorial problems. In Complexity of Computer Computations, pages 85-103. Springer, 1972. Google Scholar
  25. Hans Kellerer, Ulrich Pferschy, and David Pisinger. Knapsack problems. Springer, 2004. Google Scholar
  26. Ketan Khowala, Ahmet B. Keha, and John Fowler. A comparison of different formulations for the non-preemptive single machine total weighted tardiness scheduling problem. In Proc. of the 2nd Multidisciplinary International Conference on Scheduling: Theory and Application (MISTA), pages 643-651, 2008. Google Scholar
  27. Konstantinos Koiliaris and Chao Xu. A faster pseudopolynomial time algorithm for subset sum. ACM Transaction on Algorithms, 15(3):40:1-40:20, 2019. Google Scholar
  28. Mikhail Y. Kovalyov and Erwin Pesch. A generic approach to proving NP-hardness of partition type problems. Discrete Applied Mathematics, 158(17):1908-1912, 2010. Google Scholar
  29. Eugene L. Lawler and James M. Moore. A functional equation and its application to resource allocation and sequencing problems. Management Science, 16(1):77-84, 1969. Google Scholar
  30. Silvano Martello and Paolo Toth. Knapsack problems: algorithms and computer implementations. John Wiley & Sons, Inc., 1990. Google Scholar
  31. Yunpeng Pan and Leyuan Shi. On the equivalence of the max-min transportation lower bound and the time-indexed lower bound for single-machine scheduling problems. Mathematical Programming, 110(3):543-559, 2007. Google Scholar
  32. Michael Pinedo. Scheduling: Theory, Algorithms and Systems. Prentice-Hall, 2008. Google Scholar
  33. David Pisinger. Linear time algorithms for knapsack problems with bounded weights. Journal of Algorithms, 32:1-14, 1999. Google Scholar
  34. Gary L. Ragatz. A branch-and-bound method for minimum tardiness sequencing on a single processor with sequence dependent setup times. In Proc. of the 24th Annual Meeting of the Decision Sciences Institute (DSI), page 1375–1377, 1993. Google Scholar
  35. Dhruv Rohatgi. Conditional hardness of earth mover distance. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), volume 145 of LIPIcs, pages 12:1-12:17, 2019. Google Scholar
  36. Michael H. Rothkopf. Scheduling independent tasks on parallel processors. Management Science, 12(5):437-447, 1966. Google Scholar
  37. Rahul Santhanam and Ryan Williams. Beating exhaustive search for quantified boolean formulas and connections to circuit complexity. In Proc. of the 26th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 231-241. SIAM, 2014. Google Scholar
  38. Dvir Shabtay, Nufar Gaspar, and Moshe Kaspi. A survey on offline scheduling with rejection. Journal of Scheduling, 16(1):3-28, 2013. Google Scholar
  39. Tianyu Wang and Odile Bellenguez. The complexity of parallel machine scheduling of unit-processing-time jobs under level-order precedence constraints. Journal of Scheduling, pages 263-269, January 2019. Google Scholar
  40. Ryan Williams. A new algorithm for optimal 2-constraint satisfaction and its implications. Theoretical Computer Science, 348(2-3):357-365, 2005. Google Scholar
  41. Ossama Younis and Sonia Fahmy. Constraint-based routing in the internet: Basic principles and recent research. IEEE Communications Surveys and Tutorials, 5(1):2-13, 2003. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail