### Abstract

We present a polynomial time domain scaling algorithm for the minimization of an M-convex function. M-convex functions are nonlinear discrete functions with (poly)matroid structures, which are being recognized to play a fundamental role in tractable cases of discrete optimization. The novel idea of the algorithm is to use an individual scaling factor for each coordinate.

Original language | English |
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Pages (from-to) | 21-35 |

Number of pages | 15 |

Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Volume | 2337 LNCS |

Publication status | Published - 2002 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

**A coordinatewise domain scaling algorithm for M-convex function minimization.** / Tamura, Akihisa.

Research output: Contribution to journal › Article

}

TY - JOUR

T1 - A coordinatewise domain scaling algorithm for M-convex function minimization

AU - Tamura, Akihisa

PY - 2002

Y1 - 2002

N2 - We present a polynomial time domain scaling algorithm for the minimization of an M-convex function. M-convex functions are nonlinear discrete functions with (poly)matroid structures, which are being recognized to play a fundamental role in tractable cases of discrete optimization. The novel idea of the algorithm is to use an individual scaling factor for each coordinate.

AB - We present a polynomial time domain scaling algorithm for the minimization of an M-convex function. M-convex functions are nonlinear discrete functions with (poly)matroid structures, which are being recognized to play a fundamental role in tractable cases of discrete optimization. The novel idea of the algorithm is to use an individual scaling factor for each coordinate.

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UR - http://www.scopus.com/inward/citedby.url?scp=84868652833&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84868652833

VL - 2337 LNCS

SP - 21

EP - 35

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -