By leveraging inference-time scaling and a novel "reflection" mechanism, ALE-Agent solves the context-drift problems that ...

Optimal stopping problems constitute a pivotal area in applied mathematics and statistics, where the objective is to determine the most opportune moment to terminate a stochastic process in order to ...

We had our first taste of the problem with mean-variance optimization at a hedge fund some years back. We loaded the positions into an optimizer, pressed the button, and discovered 25% of the ...

We might be witnessing the start of a new computing era where AI, cloud and quantum begin to converge in ways that redefine ...

Over the course of my 25-year career in the mathematical optimization software industry, I’ve lost count of how many times I’ve been asked this question: “Can you tell me what mathematical ...

A framework based on advanced AI techniques can solve complex, computationally intensive problems faster and in a more more scalable way than state-of-the-art methods, according to a new study. A ...

where \(\mathsf{G}(\cdot)\) is some convex operator and \(\mathcal{F}\) is as set of feasible input distributions. Examples of such an optimization problem include finding capacity in information ...

Dr. James McCaffrey of Microsoft Research shows how to implement simulated annealing for the Traveling Salesman Problem (find the best ordering of a set of discrete items). The goal of a combinatorial ...

A Korean researcher who solved the “Moving Sofa Problem,” a mathematical challenge that had puzzled mathematicians for nearly ...