Ararat, ÇağınUlus, FirdevsUmer, Muhammad2025-02-242025-02-242024-07-251052-6234https://hdl.handle.net/11693/116744In this work, we propose an outer approximation algorithm for solving bounded convex vector optimization problems (CVOPs). The scalarization model solved iteratively within the algorithm is a modification of the norm-minimizing scalarization proposed in [\c C. Ararat, F. Ulus, and we prove that the algorithm terminates after finitely many iterations, and it returns a polyhedral outer approximation to the upper image of the CVOP such that the Hausdorff distance between the two is less than \epsilon . We show that for an arbitrary norm used in the scalarization models, the approximation error after k iterations decreases by the order of O(k1/(1-q)), where q is the dimension of the objective space. An improved convergence rate of O(k2/(1-q)) is proved for the special case of using the Euclidean norm.EnglishCC BY 4.0 (Attribution 4.0 International)https://creativecommons.org/licenses/by/4.0/Convex vector optimizationMultiobjective optimizationApproximation algorithmConvergence rateConvex compact setHausdorff distanceArticle10.1137/23M15745801095-7189