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

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.

Abstract: The dual challenges of high communication costs and gradient inaccessibility–common in privacy-sensitive systems or black-box environments–drive our work on communication-constrained, ...

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 ...

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 ...