Memoization method for storing of minimum-weight triangulation of a convex polygon

This study presents a practical view of dynamic programming, specifically in the context of the application of finding the optimal solutions for the polygon triangulation problem. The problem of the optimal triangulation of polygon is considered to be as a recursive substructure. The basic idea of the constructed method lies in finding to an adequate way for a rapid generation of optimal triangulations and storing - them in as small as possible memory space. The upgraded method is based on a memoization technique, and its emphasis is in storing the results of the calculated values and returning the cached result when the same values again occur. The significance of the method is in the generation of the optimal triangulation for a large number of n. All the calculated weights in the triangulation process are stored and performed in the same table. Results processing and implementation of the method was carried out in the Java environment and the experimental results were compared with the square matrix and Hurtado-Noy method.

[1]  Xianfeng Gu,et al.  Discrete Surface Ricci Flow , 2008, IEEE Transactions on Visualization and Computer Graphics.

[2]  Raimund Seidel,et al.  A Simple and Fast Incremental Randomized Algorithm for Computing Trapezoidal Decompositions and for Triangulating Polygons , 1991, Comput. Geom..

[3]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[4]  P. Stanimirović,et al.  Method for finding and storing optimal triangulations based on square matrix , 2018 .

[5]  G. Klincsek Minimal Triangulations of Polygonal Domains , 1980 .

[6]  Marco Attene,et al.  Polygon mesh repairing: An application perspective , 2013, CSUR.

[7]  Peter Gilbert New Results on Planar Triangulations. , 1979 .

[8]  Bernard Chazelle Triangulating a simple polygon in linear time , 1991, Discret. Comput. Geom..

[9]  Finding optimal triangulation based on block method , 2014, SOCO 2014.

[10]  Micha Sharir,et al.  Filling gaps in the boundary of a polyhedron , 1995, Comput. Aided Geom. Des..

[11]  Muzafer Saračević,et al.  Block Method for Convex Polygon Triangulation , 2013 .

[12]  Gerhard Roth,et al.  An Efficient Volumetric Method for Building Closed Triangular Meshes from 3-D Image and Point Data , 1997, Graphics Interface.

[13]  Christopher Batty,et al.  Tetrahedral Embedded Boundary Methods for Accurate and Flexible Adaptive Fluids , 2010, Comput. Graph. Forum.

[14]  Mathieu Desbrun,et al.  Blue noise through optimal transport , 2012, ACM Trans. Graph..

[15]  M. Saračević,et al.  Computational Geometry Applications , 2018, Southeast Europe Journal of Soft Computing.

[16]  Kenneth Rose,et al.  Developable surfaces from arbitrary sketched boundaries , 2007, Symposium on Geometry Processing.

[17]  Lu Liu,et al.  Surface Reconstruction From Non‐parallel Curve Networks , 2008, Comput. Graph. Forum.

[18]  David Eppstein,et al.  On triangulating three-dimensional polygons , 1998, Comput. Geom..

[19]  Alla Sheffer,et al.  Design-driven quadrangulation of closed 3D curves , 2012, ACM Trans. Graph..

[20]  C. Mercat Discrete Riemann Surfaces and the Ising Model , 2001, 0909.3600.

[21]  Muzafer Saračević,et al.  Convex polygon triangulation based on planted trivalent binary tree\\ and ballot problem , 2019 .