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On-line 𝓟-coloring of graphs

100%

Borowiecki P.

Discussiones Mathematicae Graph Theory

2006

tom 26

nr 3

389-401

For a given induced hereditary property 𝓟, a 𝓟-coloring of a graph G is an assignment of one color to each vertex such that the subgraphs induced by each of the color classes have property 𝓟. We consider the effectiveness of on-line 𝓟-coloring algorithms and give the generalizations and extensions of selected results known for on-line proper coloring algorithms. We prove a linear lower bound for the performance guarantee function of any stingy on-line 𝓟-coloring algorithm. In the class of generalized trees, we characterize graphs critical for the greedy 𝓟-coloring. A class of graphs for which a greedy algorithm always generates optimal 𝓟-colorings for the property 𝓟 = K₃-free is given.

An algorithm for 1-space bounded cube packing

75%

Zielonka Ł.

Annals of Computer Science and Information Systems

2019

tom Vol. 18

171--175

In this paper, we present a 1-space bounded cube packing algorithm with asymptotic competitive ratio 10.872.

A survey of hard-to-color graphs for off-line and on-line model of vertex coloring

63%

Borowiecki P. , Kubale M.

Journal of Applied Computer Science

2001

tom Vol. 9, nr 2

7-17

In the paper we review the most popular on-line and off-line graph coloring algorithms. For each algorithm we give: short description. performance guarantee, the smallest HC and slightly HC graphs, positive cases and negative cases. Finally, we give the smallest benchmark for off-line sequential algorithms and the smallest weak benchmark for on-line algorithms.

On-line adaptive learning rate BP algorithm for MLP and application to an identification problem

51%

Sha D. H. , Bajić V. B.

Journal of Applied Computer Science

1999

tom Vol. 7, nr 2

67-82

An on-line algorithm that uses an adaptive learning rate is proposed. Its development is based on the analysis of the convergence of the conventional gradient descent method for three-layer BP neural networks. The effectiveness of the proposed algorithm applied to the identification and prediction of behavior of non-linear dynamic systems is demonstrated by simulation experiments.