Convexification Technique and Portfolio Optimization

摘要

In this paper, a general transformation method which converts a nonconvex optimization problem to an equivalent problem with better properties is proposed. Under certain assumptions, the local convexity of the Lagrangian function of the equivalent problem is guaranteed and thus the class of optimization models to which dual methods can be applied is extended. Practical classes of problems where the proposed method can be applied are given. They include the class of portfolio selection models. Numerical examples illustrate the main results.