A new global optimization method for a symmetric Lipschitz continuous function and the application to searching for a globally optimal partition of a one-dimensional set
The paper proposes a method for solving a global optimization problem for a symmetric Lipschitz continuous function. The author shows that this problem always has a solution with natural conditions on the data. The proposed method is illustrated by solving a center-based clustering problem with synthetic data. Some numerical experiments are presented by testing the proposed method on the image segmentation problem.
scientific article; zbMATH DE number 6802511
scientific article