For a regular polyhedron (or polygon) centered at the origin, the coordinates of the vertices are eigenvectors of the graph Laplacian for the skeleton of that polyhedron (or polygon) associated with the first (non-trivial) eigenvalue. In this paper, we generalize this relationship. For a given graph, we study the eigenvalue optimization problem of maximizing the first (non-trivial) eigenvalue of the graph Laplacian over non-negative edge weights. We show that the spectral realization of the graph using the eigenvectors corresponding to the solution of this problem, under certain assumptions, is a centered, unit-distance graph realization that has maximal total variance. This result gives a new method for generating unit-distance graph realizations and is based on convex duality. A drawback of this method is that the dimension of the realization is given by the multiplicity of the extremal eigenvalue, which is typically unknown prior to solving the eigenvalue optimization problem. Our results are illustrated with a number of examples.
翻译:对于以源为中心的一个普通多边面体(或多边形),顶部的坐标是图 Laplacecian 与第一个(非三相) 元值相关联的多面体骨骼(或多边形)的图 Laplacian 的光源值。在本文中,我们概括了这一关系。对于一个特定的图,我们研究了将图的第一次(非三相) egen值最大化至非负边缘重量的顶值最大化的元值问题。我们显示,在某些假设下,使用与这一问题的解决方案相对应的图象的光谱化成像,是具有最大全差异的中央、单位-距离图成像。结果为生成单位-远方图的实现提供了新方法,并以等离子二元性为基础。这种方法的一个缺点是,实现该值的维度是由极地平面值的多度所给出的,在解决电子值优化问题之前通常未知。我们的结果用数字来说明。