Mean-Variance (MV) theory for portfolio selection is based on assumptions involving parameters that have to be estimated using historical data. Depending on the method of estimation, the estimates will suffer from estimation error and/or specification error, both of which will effect the portfolio optimization in such a way that the resulting optimal portfolio is not the true optimal portfolio....

Portfolio selection is vulnerable to the error-amplifying effects of combining optimization with statistical estimation and model error. For dynamic portfolio control, sources of model error include the evolution of market factors and the influence of these factors on asset returns. We develop portfolio control rules that are robust to this type of uncertainty, applying a stochastic notion of r...

Value-at-Risk (VaR) is one of the most widely accepted risk measures in the financial and insurance industries, yet efficient optimization of VaR remains a very difficult problem. We propose a computationally tractable approximation method for minimizing the VaR of a portfolio based on robust optimization techniques. The method results in the optimization of a modified VaR measure, Asymmetry-Ro...

optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...

due to project evaluation complexity and resource constraints, the project portfolio optimization is numerous decision making challenges. hence, many researches have been done to introduce model and methods for portfolio optimization. but most of them have not considered the interaction between projects. considering the interactions between projects increase complexity of portfolio optimization...

Financial decision making involves uncertainty and consequently risk. It is well known that asset return forecasts and risk estimates are inherently inaccurate. The inaccuracy in forecasting and estimation can be addressed through the specification of rival scenarios. In this paper, we extend the multi-period mean-variance portfolio optimization and asset liability management problems to the ro...

Based on weighted signal covariance (WSC) matrix and maximum likelihood (ML) estimation, a directionof-arrival (DOA) estimation method of multiple moving targets is designed and named as WSC-ML in the presence of impulse noise. In order to overcome the shortcoming of the multidimensional search cost of maximum likelihood estimation, a novel continuous quantum particle swarm optimization (QPSO) ...

Recently, coherent risk measure minimization was formulated as robust optimization and the correspondence between coherent risk measures and uncertainty sets of robust optimization was investigated. We study minimizing coherent risk measures under a norm equality constraint with the use of robust optimization formulation. Not only existing coherent risk measures but also a new coherent risk mea...

In linear regression, we need to avoid adding too much richness to the model. Therefore we need feature selection, or regularization to make our fitting curve smoother. Qualitatively, the original linear regression model is an optimization problem of the form min w m i=1 (w · x i − y i) 2 And the corresponding regularized version of the same problem is min w m i=1 (w · x i − y i) 2 + λw 2 2 , w...