This book explains gradient-based stochastic optimization, exploiting the methodologies of stochastic approximation and gradient estimation. Although the approach is theoretical, the book emphasizes ...

Abstract: Our paper presents a stochastic multi-objective optimization model {minimize cost, maximize profits} designed to manage supply and demand uncertainties in a two-tier supply chain. The model ...

This thesis provides an overview of adaptive stochastic gradient descent methods for large-scale optimization, and elaborates on their background, theoretical properties and practical performance.

The following problem models are implemented in the subdirectories. Each implementation contains three components: model: implements simulator based on how the problem is modeled policy: implements ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...

Would you choose to complete a task in a few seconds for a guaranteed reward, or spend half an hour exploring unknown paths that may or may not lead to something better? Using a multistep ...

Ignacio Luján proposes a pricing framework for multi-asset derivatives based on the family of normal mean-variance mixture copulas. This class of copulas offers sufficient flexibility to capture a ...

Lauren (Hansen) Holznienkemper is a lead editor for the small business vertical at Forbes Advisor, specializing in HR, payroll and recruiting solutions for small businesses. Using research and writing ...