## Abstract

In discrete convex analysis, the scaling and proximity properties for the class of L^{♮}-convex functions were established more than a decade ago and have been used to design efficient minimization algorithms. For the larger class of integrally convex functions of n variables, we show here that the scaling property only holds when n ≤ 2, while a proximity theorem can be established for any n, but only with an exponential bound. This is, however, sufficient to extend the classical logarithmic complexity result for minimizing a discretely convex function in one dimension to the case of integrally convex functions in two dimensions. Furthermore, we identified a new class of discrete convex functions, called directed integrally convex functions, which is strictly between the classes of L^{♮}-convex and integrally convex functions but enjoys the same scaling and proximity properties that hold for L^{♮}-convex functions.

Original language | English |
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Title of host publication | 27th International Symposium on Algorithms and Computation, ISAAC 2016 |

Publisher | Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing |

Pages | 57.1-57.13 |

Volume | 64 |

ISBN (Electronic) | 9783959770262 |

DOIs | |

Publication status | Published - 2016 Dec 1 |

Event | 27th International Symposium on Algorithms and Computation, ISAAC 2016 - Sydney, Australia Duration: 2016 Dec 12 → 2016 Dec 14 |

### Other

Other | 27th International Symposium on Algorithms and Computation, ISAAC 2016 |
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Country | Australia |

City | Sydney |

Period | 16/12/12 → 16/12/14 |

## Keywords

- Discrete convexity
- Discrete optimization
- Proximity theorem
- Scaling algorithm

## ASJC Scopus subject areas

- Software