G-2020-50
Largest small polygons: A sequential convex optimization approach
référence BibTeX
A small polygon is a polygon of unit diameter. The maximal area of a small polygon with n=2m
vertices is not known when m≥7
. Finding the largest small n
-gon for a given number n≥3
can be formulated as a nonconvex quadratically constrained quadratic optimization problem. We propose to solve this problem with a sequential convex optimization approach, which is a ascent algorithm guaranteeing convergence to a locally optimal solution. Numerical experiments on polygons with up to n=128
sides suggest that the optimal solutions obtained are near-global. Indeed, for even 6≤n≤12
, the algorithm proposed in this work converges to known global optimal solutions found in the literature.
Paru en octobre 2020 , 12 pages
Ce cahier a été révisé en mai 2021
Document
G2050R.pdf (410 Ko)