We investigate a robust version of the portfolio selection problem under a risk measure based on the lower-partial moment (LPM), where uncertainty exists in the underlying distribution. We demonstrate that the problem formulations for robust portfolio selection based on the worst-case LPMs of...

Based on cluster analysis, a novel method is introduced in this paper to generate multistage scenarios. A linear programming model is proposed to exclude the arbitrage opportunity by appending a scenario to the generated scenario set. By means of a cited stochastic linear goal programming...

Robust optimization, one of the most popular topics in the field of optimization and control since the late 1990s, deals with an optimization problem involving uncertain parameters. In this paper, we consider the relative robust conditional value-at-risk portfolio selection problem where the...

Portfolio risk can be decomposed into two parts, the systematic risk and the nonsystematic risk. It is well known that the nonsystematic risk can be eliminated by diversification, while the systematic risk cannot. Thus, the portfolio risk, except for that of undiversified small portfolios, is...

As the skewed return distribution is a prominent feature in nonlinear portfolio selection problems which involve derivative assets with nonlinear payoff structures, Value-at-Risk (VaR) is particularly suitable to serve as a risk measure in nonlinear portfolio selection. Unfortunately, the...

We develop in this paper a novel portfolio selection framework with a feature of double robustness in both return distribution modeling and portfolio optimization. While predicting the future return distributions always represents the most compelling challenge in investment, any underlying...

Complex polynomial optimization problems arise from real-life applications including radar code design, MIMO beamforming, and quantum mechanics. In this paper, we study complex polynomial optimization models where the objective function takes one of the following three forms: (1) multilinear;...

In this paper we generalize the primal--dual cone affine scaling algorithm of Sturm and Zhang to semidefinite programming.We show in this paper that the underlying ideas of the cone affine scaling algorithm can be naturely applied to semidefiniteprogramming, resulting in a new algorithm....

In this paper we discuss a locational model with a profit-maximizing objective. The model can be illustrated by the followingsituation. There is a set of potential customers in a given region. A firm enters the market and wants to sell a certainproduct to this set of customers. The location and...

In this paper we consider optimization problems defined by a quadraticobjective function and a finite number of quadratic inequality constraints.Given that the objective function is bounded over the feasible set, we presenta comprehensive study of the conditions under which the optimal solution...