### Abstract

We present a new derivation of efficient algorithms for a class of optimization problems called maximum marking problems. We extend the class of weight functions used in the specification to allow for weight functions with accumulation, which is particularly useful when the weight of each element depends on adjacent elements. This extension of weight functions enables us to treat more interesting optimization problems such as a variant of the maximum segment sum problem and the fair bonus distribution problem. The complexity of the derived algorithm is linear with respect to the size of the input data.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Pages | 562-578 |

Number of pages | 17 |

Volume | 3722 LNCS |

DOIs | |

Publication status | Published - 2005 |

Externally published | Yes |

Event | 2nd International Colloquium on Theoretical Aspects of Computing - ICTAC 2005 - Hanoi Duration: 2005 Oct 17 → 2005 Oct 21 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 3722 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 2nd International Colloquium on Theoretical Aspects of Computing - ICTAC 2005 |
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City | Hanoi |

Period | 05/10/17 → 05/10/21 |

### Fingerprint

### Keywords

- Accumulative weight function
- Maximum marking problem
- Optimization problem
- Program derivation

### ASJC Scopus subject areas

- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 3722 LNCS, pp. 562-578). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3722 LNCS). https://doi.org/10.1007/11560647_37

**Maximum marking problems with accumulative weight functions.** / Sasano, Isao; Ogawa, Mizuhito; Hu, Zhenjiang.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 3722 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3722 LNCS, pp. 562-578, 2nd International Colloquium on Theoretical Aspects of Computing - ICTAC 2005, Hanoi, 05/10/17. https://doi.org/10.1007/11560647_37

}

TY - GEN

T1 - Maximum marking problems with accumulative weight functions

AU - Sasano, Isao

AU - Ogawa, Mizuhito

AU - Hu, Zhenjiang

PY - 2005

Y1 - 2005

N2 - We present a new derivation of efficient algorithms for a class of optimization problems called maximum marking problems. We extend the class of weight functions used in the specification to allow for weight functions with accumulation, which is particularly useful when the weight of each element depends on adjacent elements. This extension of weight functions enables us to treat more interesting optimization problems such as a variant of the maximum segment sum problem and the fair bonus distribution problem. The complexity of the derived algorithm is linear with respect to the size of the input data.

AB - We present a new derivation of efficient algorithms for a class of optimization problems called maximum marking problems. We extend the class of weight functions used in the specification to allow for weight functions with accumulation, which is particularly useful when the weight of each element depends on adjacent elements. This extension of weight functions enables us to treat more interesting optimization problems such as a variant of the maximum segment sum problem and the fair bonus distribution problem. The complexity of the derived algorithm is linear with respect to the size of the input data.

KW - Accumulative weight function

KW - Maximum marking problem

KW - Optimization problem

KW - Program derivation

UR - http://www.scopus.com/inward/record.url?scp=33646409556&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33646409556&partnerID=8YFLogxK

U2 - 10.1007/11560647_37

DO - 10.1007/11560647_37

M3 - Conference contribution

AN - SCOPUS:33646409556

SN - 3540291075

SN - 9783540291077

VL - 3722 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 562

EP - 578

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -