Students at Heydon Park Secondary, which serves girls with special needs, will relocate in January. But parents fear for the ...

Abstract: The branch and bound (BB) algorithm is widely used to obtain the global solution of mixed-integer linear programming (MILP) problems. On the other hand, when the traditional BB structure is ...

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...

HiOp is an optimization solver for solving certain mathematical optimization problems expressed as nonlinear programming problems. HiOp is a lightweight HPC solver that leverages application's ...

Optimal control problems are pervasive across numerous disciplines, including engineering, economics, biology, and medicine, where they play a crucial role in optimizing the performance of dynamic ...

Abstract: This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are ...

Understanding the mechanism of how neural networks learn features from data is a fundamental problem in machine learning. Our work explicitly connects the mechanism of neural feature learning to a ...

Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States Department of Chemical and Biological Engineering, Princeton University, Princeton, ...

In the last decade, there have grown a number of applications of neural networks and machine learning. Based on the success of the neural network and machine learning applications in the real-world ...

This work addresses the problem of reference tracking in autonomously learning robots with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and ...