We want to choose an intermediate approach between stochastic optimization, which has no robustness to the error of distribution; and robust optimization, which ignores available problem data. where we consider a set Γ of density functions or distributions, and maximize the worst-case expected cost value among those distributions in Γ.

The purpose of this tutorial is to illustrate the capabilities of the robust and data-driven op- timization framework as a modeling tool in decision-making under uncertainty, and in particular to:

Mar 18, 2025 · By bridging optimization theory and data-driven methodologies, this review outlines strategies to advance data-driven optimization across diverse fields, offering insights to foster interdisciplinary collaboration and guide future research.

Jan 24, 2023 · We bridge this gap by introducing a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We introduce two classes of explanations and develop methods to find nearest explanations of …

Feb 21, 2021 · Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be expressed as the minimizer of a surrogate optimization model constructed from the data. The quality of a data-driven decision is measured by its out-of-sample risk.

Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.

Sep 6, 2018 · In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization.

Mar 8, 2021 · We present two methods, based on regression in reproducing kernel Hilbert spaces, for solving an optimization problem with uncertain parameters for which we have historical data, including auxiliary data. The first method approximates the objective function and the second approximates the optimizer.

Feb 22, 2024 · Advanced analytics empowers organizations to extract valuable insights from vast data, enabling informed decision-making in streamlining IT operations. So, how do you adopt a data-driven approach to IT optimization? What are the key metrics, challenges, and benefits of using data for optimizing your workflows?

Jan 1, 2024 · Data-driven optimization emerged as an alternative to traditional optimization practices by developing efficient algorithms that use data to explore the best solution of a given problem.