Applied Intuition is looking to automate mining and logistics, and it is acknowledging how difficult it could be to give AI a ...

University of Tennessee researchers James Ostrowski and Rebekah Herrman are developing quantum-computing tools to tackle multi-stage stochastic decision problems in fields like energy, logistics, and ...

Abstract: This article considers modern applied problems formalized as the Quadratic Assignment Problem (QAP) and its generalizations. The main mathematical formulations are analyzed, with particular ...

Optimization problems often involve situations in which the user's goal is to minimize and/or maximize not a single objective function, but several, usually conflicting, functions simultaneously. Such ...

A line of engineering research seeks to develop computers that can tackle a class of challenges called combinatorial optimization problems. These are common in real-world applications such as ...

In the context of mass higher education, Chinese application-oriented undergraduate institutions face significant teaching challenges stemming from the increasingly diverse student population. This ...

ABSTRACT: In this paper, we investigate the convergence of the generalized Bregman alternating direction method of multipliers (ADMM) for solving nonconvex separable problems with linear constraints.

ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...

Impact Statement: The multi-objective traveling salesman problem (MOTSP), one of the typical combinatorial optimization problems, can be used to model a broad range of real applications.