In many applications, when building linear regression models, it is important to account for the presence of outliers, i.e., corrupted input data points. Such problems can be formulated as mixed-integer optimization problems involving cubic terms, each given by the product of a binary variable and a quadratic term of the continuous variables. Existing approaches in … Read more
We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guarantees of revenue adequacy, cost recovery, and price distortion in … Read more
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Critical operations often involve stakeholders with diverse perspectives, yet centralized optimization assumes participation or private information, neither of which is a priori guaranteed. Additionally, decision-making involves discrete decisions, making optimization computationally challenging. Centralized formulations use approximations to manage complexity, often overlooking stakeholder perspectives, leading to bias. To resolve these challenges, we adopt a privacy-aware participatory-distributed … Read more
This paper offers a new standard, called MOSDEX (Mathematical Optimization Solver Data EXchange), for managing the interaction of data with solvers for mathematical optimization. The rationale for this standard is to take advantage of modern software tools that can efficiently handle very large datasets that have become the norm for data analytics in the past … Read more