Skip to main content

Integer Conic Function Minimization Based on the Comparison Oracle

  • Conference paper
  • First Online:
Mathematical Optimization Theory and Operations Research (MOTOR 2019)

Abstract

Let \(f : \mathbb {R}^n \rightarrow \mathbb {R}\) be a conic function and \(x_0 \in \mathbb {R}^n\). In this note, we show that the shallow separation oracle for the set \(K = \{x \in \mathbb {R}^n : f(x) \le f(x_0)\}\) can be polynomially reduced to the comparison oracle of the function f. Combining these results with known results of D. Dadush et al., we give an algorithm with \((O(n))^n \log R\) calls to the comparison oracle for checking the non-emptiness of the set \(K \cap \mathbb {Z}^n\), where K is included to the Euclidean ball of a radius R. Additionally, we give a randomized algorithm with the expected oracle complexity \((O(n))^n \log R\) for the problem to find an integral vector that minimizes values of f on an Euclidean ball of a radius R. It is known that the classes of convex, strictly quasiconvex functions, and quasiconvex polynomials are included into the class of conic functions. Since any system of conic functions can be represented by a single conic function, the last facts give us an opportunity to check the feasibility of any system of convex, strictly quasiconvex functions, and quasiconvex polynomials by an algorithm with \((O(n))^n \log R\) calls to the comparison oracle of the functions. It is also possible to solve a constraint minimization problem with the considered classes of functions by a randomized algorithm with \((O(n))^n \log R\) expected oracle calls.

This research is supported by Russian Foundation for Basic Research (Project 18-31-20001-mol-a-ved).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmadi, A., Olshevsky, A., Parrilo, P., Tsitsiklis, J.: NP-hardness of deciding convexity of quadratic polynomials and related problems. Math. Program. 137(1–2), 453–476 (2013). https://doi.org/10.1007/s10107-011-0499-2

    Article  MathSciNet  MATH  Google Scholar 

  2. Basu, A., Oertel, T.: Centerpoints: a link between optimization and convex geometry. SIAM J. Optim. 27(2), 866–889 (2017). https://doi.org/10.1007/978-3-319-33461-5_2

    Article  MathSciNet  MATH  Google Scholar 

  3. Bredereck, R., Faliszewski, P., Niedermeier, R., Skowron, P., Talmon, N.: Mixed integer programming with convex/concave constraints: fixed-parameter tractability and applications to multicovering and voting. CoRR, https://arxiv.org/abs/1709.02850 (2017)

  4. Chirkov, A.: Minimization of quasi-convex function on two-dimensional integer lattice. Vestn. Nizhegorod. Univ. N. I. Lobachevskogo, Mat. Model. Optim. Upr. 1, 227–238 (2003). (in Russian)

    Google Scholar 

  5. Chirkov, A., Gribanov, D., Malyshev, D., Pardalos, P., Veselov, S., Zolotykh, A.: On the complexity of quasiconvex integer minimization problem. J. Glob. Optim. 73(4), 761–788 (2019). https://doi.org/10.1007/s10898-018-0729-8

    Article  MathSciNet  Google Scholar 

  6. Dadush, D.: Integer programming, lattice algorithms, and deterministic volume estimation. ProQuest LLC, Ann Arbor, MI. thesis (Ph.D.), Georgia Institute of Technology (2012)

    Google Scholar 

  7. Dadush, D., Peikert, C., Vempala, S.: Enumerative lattice algorithms in any norm via M-ellipsoid coverings. In: Proceedings of the 52nd Annual IEEE Symposium on Foundations of Computer Science (FOCS 11), pp. 580–589 (2011). https://doi.org/10.1109/FOCS.2011.31

  8. De Loera, J., Hemmecke, R., Koppe, M., Weismantel, R.: Integer polynomial optimization in fixed dimension. Math. Oper. Res. 31(1), 147–153 (2006). https://doi.org/10.1287/moor.1050.0169

    Article  MathSciNet  MATH  Google Scholar 

  9. Grötschel, M., Lovász, L., Schrijver, A.: Geometric Algorithms and Combinatorial Optimization. Algorithms and Combinatorics, vol. 2, 2nd edn. Springer, Berlin (1993). https://doi.org/10.1007/978-3-642-78240-4. corrected ed

    Book  MATH  Google Scholar 

  10. Heinz, S.: Complexity of integer quasiconvex polynomial optimization. J. Complex. 21(4), 543–556 (2005)

    Article  MathSciNet  Google Scholar 

  11. Heinz, S.: Quasiconvex functions can be approximated by quasiconvex polynomials. ESAIM Control Optim. Calc. Var. 14(4), 795–801 (2008). https://doi.org/10.1051/cocv:2008010

    Article  MathSciNet  MATH  Google Scholar 

  12. Hemmecke, R., Onn, S., Weismantel, R.: A polynomial oracle-time algorithm for convex integer minimization. Math. Program. 126(1), 97–117 (2011). https://doi.org/10.1007/s10107-009-0276-7

    Article  MathSciNet  MATH  Google Scholar 

  13. Hildebrand, R., Köppe, M.: A new Lenstra-type algorithm for quasiconvex polynomial integer minimization with complexity \(2^{O(n \log n)}\). Discret. Optim. 10(1), 69–84 (2013). https://doi.org/10.1016/j.disopt.2012.11.003

    Article  MathSciNet  MATH  Google Scholar 

  14. Khachiyan, L., Porkolab, L.: Integer optimization on convex semialgebraic sets. Discret. Comput. Geom. 23(2), 207–224 (2000). https://doi.org/10.1007/PL00009496

    Article  MathSciNet  MATH  Google Scholar 

  15. Lenstra, H.: Integer programming with a fixed number of variables. Math. Oper. Res. 8(4), 538–548 (1983). https://doi.org/10.1287/moor.8.4.538

    Article  MathSciNet  MATH  Google Scholar 

  16. Nemirovski, A., Yudin, D.: Evaluation of the information complexity of mathematical programming problems. Ekonomika i Matematicheskie Metody 13(2), 3–45 (1976). (in Russian)

    Google Scholar 

  17. Nemirovsky, A., Yudin, D.: Problem Complexity and Method Efficiency in Optimization. Wiley, New York (1983)

    Google Scholar 

  18. Oertel, T.: Integer convex minimization in low dimensions. Thes. doct. phylosophy. Eidgenössische Technische Hochschule, Zürich (2014)

    Google Scholar 

  19. Oertel, T., Wagner, C., Weismantel, R.: Convex integer minimization in fixed dimension. https://arxiv.org/pdf/1203.4175.pdf (2012)

  20. Oertel, T., Wagner, C., Weismantel, R.: Integer convex minimization by mixed integer linear optimization. Oper. Res. Lett. 42(6), 424–428 (2014). https://doi.org/10.1016/j.orl.2014.07.005

    Article  MathSciNet  MATH  Google Scholar 

  21. Veselov, S., Gribanov, D., Zolotykh, N., Malishev, D., Chirkov, A.: Minimizing a symmetric quasiconvex function on a two-dimensional lattice. J. Appl. Ind. Math. 12(3), 587–594 (2018). https://doi.org/10.1134/S199047891803016X

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dmitriy V. Gribanov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gribanov, D.V., Malyshev, D.S. (2019). Integer Conic Function Minimization Based on the Comparison Oracle. In: Khachay, M., Kochetov, Y., Pardalos, P. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Lecture Notes in Computer Science(), vol 11548. Springer, Cham. https://doi.org/10.1007/978-3-030-22629-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22629-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22628-2

  • Online ISBN: 978-3-030-22629-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics