Robust Security Constrained ACOPF via Conic Programming: Identifying the Worst Contingencies

Worst-Case Value-At-Risk and Robust Portfolio Optimization: A Conic Programming Approach

Classical formulations of the portfolio optimization problem, such as mean-variance or Value-at-Risk (VaR) approaches, can result in a portfolio extremely sensitive to errors in the data, such as mean and covariance matrix of the returns. In this paper we propose a way to alleviate this problem in a tractable manner. We assume that the distribution of returns is partially known, in the sense th...

Robust portfolio optimization: a conic programming approach

The Markowitz Mean Variance model (MMV) and its variants are widely used for portfolio selection. The mean and covariance matrix used in the model originate from probability distributions that need to be determined empirically. It is well known that these parameters are notoriously difficult to estimate. In addition, the model is very sensitive to these parameter estimates. As a result, the per...

Multi-Task Learning via Conic Programming

When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and has been studied extensively. Existing approaches to MTL often treat all the tasks as uniformly related to each other and the relatedness of the tasks is controlled globally. For this reason, the existing methods can ...

Pattern separation via ellipsoids and conic programming

Umbrella Contingencies in Security-Constrained Optimal Power Flow

The notion of umbrella contingencies is rigorously defined in the context of the security-constrained optimal power flow (SCOPF) problem in both its deterministic and probabilistic forms. The set of umbrella contingencies is a subset of the set of credible contingencies that is sufficient to attain levels of security and economic performance identical or nearly identical to those found when all...