Skip to main content
Log in

Toward a Model for Backtracking and Dynamic Programming

  • Published:
computational complexity Aims and scope Submit manuscript

Abstract

We propose a model called priority branching trees (pBT) for backtracking and dynamic programming algorithms. Our model generalizes both the priority model of Borodin, Nielson and Rackoff, as well as a simple dynamic programming model due to Woeginger, and hence spans a wide spectrum of algorithms. After witnessing the strength of the model, we then show its limitations by providing lower bounds for algorithms in this model for several classical problems such as Interval Scheduling, Knapsack and Satisfiability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Dimitris Achlioptas & Gregory B. Sorkin (2000). Optimal myopic algorithms for random 3-SAT. In IEEE Symposium on Foundations of Computer Science, 590–600.

  • M. Alekhnovich (2005). Lower bounds for k-DNF Resolution on random 3CNF. In Proceedings of the 37th Symposium on Theory of Computing, 251–256.

  • M. Alekhnovich, E. Hirsch & D. Itsykson (2005). Exponential lower bounds for the running time of DPLL algorithms on satisfiable formulas. Journal of Automated Reasoning 35(1-3), 51–72. Preliminary version appeared in Proceedings of the 31st International Colloquium on Automata, Languages and Programming (ICALP), 2004.

    Google Scholar 

  • M. Alekhnovich & A. Razborov (2001). Lower bounds for the polynomial calculus: non-binomial case. In Proc. 42nd Ann. Symp. on Foundations of Computer Science. IEEE Computer Society.

  • Angelopoulos S., Borodin A. (2004) On the Power of Priority Algorithms for Facility Location and Set Cover. Algorithmica 40(4): 271–291

    Article  MATH  MathSciNet  Google Scholar 

  • Arkin E.M., Silverberg E.L. (1987) Scheduling Jobs with Fixed Start and End Times. Disc. Appl. Math 18: 1–8

    Article  MATH  MathSciNet  Google Scholar 

  • Arora Sanjeev., Bollobás Béla., Lovász László., Tourlakis Iannis (2006) Proving Integrality Gaps without Knowing the Linear Program. Theory of Computing 2(2): 19–51

    Article  MathSciNet  Google Scholar 

  • Arrow Kenneth (1951) Social Choice and Individual Values. Wiley, New York

    MATH  Google Scholar 

  • Borodin A., Nielsen M., Rackoff C. (2003) (Incremental) Priority Algorithms. Algorithmica 37: 295–326

    Article  MATH  MathSciNet  Google Scholar 

  • Allan Borodin, Joan Boyar & Kim S. Larsen (2005). Priority Algorithms for Graph Optimization Problems. In Second Workshop on Approximation and Online Algorithms, volume 3351 of Lecture Notes in Computer Science, 126–139. Springer-Verlag.

  • J. Buresh-Oppenheim, S. Davis & R. Impagliazzo (2010). A stronger model of dynamic programming algorithms. To appear in the journal Algorithmica.

  • Chvátal V. (1985) Hard Knapsack Problems. Operations Research 28(6): 1402–1441

    Article  Google Scholar 

  • S. Cook & D. Mitchell (1997). Finding hard instances of the satisfiability problem: A survey. In DIMACS Series in Theoretical Computer Science.

  • Cormen T., Leiserson C., Rivest R., Stein C. (2001) Introduction to Algorithms, Second Edition. MIT Press, Cambridge, Mass

    Google Scholar 

  • Davis M., Logemann G., Loveland D. (1962) A machine program for theorem proving. Communications of the ACM 5: 394–397

    Article  MATH  MathSciNet  Google Scholar 

  • Davis M., Putnam H. (1960) A computing procedure for quantification theory. Communications of the ACM 7: 201–215

    MATH  MathSciNet  Google Scholar 

  • S. Davis, J. Edmonds & R.Impagliazzo (2010). Randomized priority algorithms, pBT, and free-branching pBT lower bounds. Manuscript in preparation.

  • S. Davis & R. Impagliazzo (2009). Models of Greedy Algorithms for Graph Problems. Algorithmica 54(3). Preliminary version appeared in the Proceedings of the 15th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2004.

  • B.C Dean, M.X. Goemans & J. Vondrák (2004). Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity. In it Proc. 44th Ann. Symp. on Foundations of Computer Science.

  • T. Erlebach & F.C.R. Spieksma (2002). Interval selection: Applications, algorithms, and lower bounds. Technical Report 152, Computer Engineering and Networks Laboratory, ETH.

  • J. Gu, P. W. Purdom, J. Franco & B. J. Wah (1997). Algorithms for the Satisfiability (SAT) Problem: A Survey. In Satisfiability (SAT) Problem, DIMACS, 19–151. American Mathematical Society.

  • M. Halldorsson, K. Iwama, S. Miyazaki & S. Taketomi (2002). Online Independent Sets. Theoretical Computer Science 953–962.

  • Håstad H. (2001) Some optimal inapproximability results. JACM 48: 798–859

    Article  MATH  Google Scholar 

  • Helman P. (1989) A Common Schema for Dynamic Programming and Branch and Bound Algorithms. Journal of the Association of Computing Machinery 36(1): 97–128

    Article  MATH  MathSciNet  Google Scholar 

  • Helman P., Rosenthal A. (1985) A comprehensive model of dynamic programming. SIAM J. on Algebraic and Discrete Methods 6: 319–334

    Article  MATH  MathSciNet  Google Scholar 

  • S.L. Horn (2004). One-Pass algorithms with revocable acceptances for job interval selection. MSc Thesis, University of Toronto.

  • Ibarra O., Kim C. (1975) Fast Approximation Algorithms for the Knapsack and Sum of Subset Problems. JACM 22: 463–468

    Article  MATH  MathSciNet  Google Scholar 

  • A. Kaporis, L. Kirousis & E. Lalas (2003). Selecting complementary pairs of literals. In Proc. LICS‘03 Workshop on Typical Case Complexity and Phase Transitions. citeseer.ist.psu.edu/kaporis03selecting.html.

  • Karp R.M., Held M. (1967) Finite state processes and dynamic programming. SIAM J. Applied Mathematics 15: 693–718

    Article  MATH  MathSciNet  Google Scholar 

  • Khanna S., Motwani R., Sudan M., Vazirani U. (1998) On Syntactic versus Computational Views of Approximability. SIAM Journal on Computing 28: 164–191

    Article  MATH  MathSciNet  Google Scholar 

  • Knuth D.E. (1971) Optimum binary search trees. Acta Informatica 1: 14–25

    Article  MATH  Google Scholar 

  • E. L. Lawler (1977). Fast Approximation Algorithms for Knapsack Problems. In Proc. 18th Ann. Symp. on Foundations of Computer Science. IEEE Computer Society, Long Beach, CA.

  • Rajeev Motwani & Prabhakar Raghavan (1995). Randomized Algorithms. Cambridge University Press.

  • Oded Regev (2002) Priority algorithms for makespan minimization in the subset model. Information Processing Letters 84(3): 153–157

    Article  MATH  MathSciNet  Google Scholar 

  • Vijay V. Vazirani (2001). Approximation Algorithms. Springer-Verlag.

  • Woeginger G. (2000) When does a dynamic programming formulation guarantee the existence of a fully polynomial time approximation scheme (FPTAS)?. INFORMS Journal on Computing 12: 57–75

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joshua Buresh-Oppenheim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alekhnovich, M., Borodin, A., Buresh-Oppenheim, J. et al. Toward a Model for Backtracking and Dynamic Programming. comput. complex. 20, 679–740 (2011). https://doi.org/10.1007/s00037-011-0028-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00037-011-0028-y

Keywords

Subject classification

Navigation