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Oliver Kreylos, Bernd Hamann, "On Simulated Annealing and the Construction of Linear Spline Approximations for Scattered Data," IEEE Transactions on Visualization and Computer Graphics, vol. 7, no. 1, pp. 1731, JanuaryMarch, 2001.  
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@article{ 10.1109/2945.910818, author = {Oliver Kreylos and Bernd Hamann}, title = {On Simulated Annealing and the Construction of Linear Spline Approximations for Scattered Data}, journal ={IEEE Transactions on Visualization and Computer Graphics}, volume = {7}, number = {1}, issn = {10772626}, year = {2001}, pages = {1731}, doi = {http://doi.ieeecomputersociety.org/10.1109/2945.910818}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Visualization and Computer Graphics TI  On Simulated Annealing and the Construction of Linear Spline Approximations for Scattered Data IS  1 SN  10772626 SP17 EP31 EPD  1731 A1  Oliver Kreylos, A1  Bernd Hamann, PY  2001 KW  Function approximation KW  linear splines KW  simulated annealing KW  multiresolution approximation KW  datadependent triangulation. VL  7 JA  IEEE Transactions on Visualization and Computer Graphics ER   
Abstract—We describe a method to create optimal linear spline approximations to arbitrary functions of one or two variables, given as scattered data without known connectivity. We start with an initial approximation consisting of a fixed number of vertices and improve this approximation by choosing different vertices, governed by a simulated annealing algorithm. In the case of one variable, the approximation is defined by line segments; in the case of two variables, the vertices are connected to define a Delaunay triangulation of the selected subset of sites in the plane. In a second version of this algorithm, specifically designed for the bivariate case, we choose vertex sets and also change the triangulation to achieve both optimal vertex placement and optimal triangulation. We then create a hierarchy of linear spline approximations, each one being a superset of all lowerresolution ones.
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