Gartner predicted traditional search volume will drop 25% this year as users shift to AI-powered answer engines. Google’s AI Overviews now reach more than 2 billion monthly users, ChatGPT serves 800 ...

Abstract: Recently, deep unfolding networks (DUNs) have emerged as a promising technique for image Compressive Sensing (CS) reconstruction by unfolding optimization algorithms, where each stage of the ...

Low-rank data analysis has emerged as a powerful paradigm across applied mathematics, statistics, and data science. With the rapid growth of modern datasets in size, dimensionality, and complexity, ...

Abstract: Distribution network optimization is represented by non-convex power flow equations, where traditional convex relaxation methods may lead to inaccurate or infeasible solutions. To ...

Online Set Cover and Load Balancing are central problems in online optimization, and there is a long line of work on developing algorithms for these problems with convex objectives. Although we know ...

This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses ...