Missing Value Imputation via Mathematical Optimization with Instance-and-Feature Neighborhoods

Datasets collected for analysis often contain a certain amount of incomplete instances, where some feature values are missing. Since many statistical analyses and machine learning algorithms depend on complete datasets, missing values need to be imputed in advance. Bertsimas et al. (2018) proposed a high-performance method that combines machine learning and mathematical optimization algorithms for … Read more

Optimal counterfactual explanations for k-Nearest Neighbors using Mathematical Optimization and Constraint Programming

\(\) Within the topic of explainable AI, counterfactual explanations to classifiers have received significant recent attention. We study counterfactual explanations that try to explain why a data point received an undesirable classification by providing the closest data point that would have received a desirable one. Within the context of one the simplest and most popular … Read more

A Framework for Mathematical Optimization in Microservice Architectures

In the last years, the gap between solution methods in literature and optimization running in production has increased. Agile development practices, DevOps and modern cloud-based infrastructure call for a revisit of how optimization software is developed. We review the state-of-the-art, propose a development framework that can be applied across different programming languages and modeling frameworks … Read more

Application of outer approximation to forecasting losses and scenarios in the target of portfolios with high of nonlinear risk

The purpose of this paper is to find appropriate solutions to concave quadratic programming using outer approximation algorithm, which is one of the algorithm of global optimization, in the target of the strong of concavity of object function i.e. high of nonlinear risk of portfolio. Firstly, my target model is a mathematical optimization programming to … Read more

Forecasting conceivable interest rate market scenarios and significant losses on interest rate portfolios using mathematical optimization

This study proposes a mathematical optimization programming model that simultaneously forecasts interest rate market scenarios and significant losses on interest rate market portfolios. The model includes three main components. A constraint condition is set using the Mahalanobis distance, which consists of innovation terms in a dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model that represent … Read more