On convex multiobjective programs with application to portfolio optimization

Abstract Focusing on (strictly) convex multiobjective programs (MOPs), we review some well‐established scalarizations in multiobjective programming from the perspective of parametric optimization and propose a modified hybrid scalarization suitable for a class of specially structured convex MOPs. Because multiobjective quadratic programs are a prominent class of convex MOPs due to their broad applicability, we review the state‐of‐the‐art algorithms for computing their efficient solutions. These two lines of investigation are merged to solve multiobjective portfolio optimization problems with three or more quadratic objective functions, a class of problems that has not been solved before. Computational examples are provided.