Stability problems of continuous-time recurrent neural networks have been extensively
studied, and many papers have been published in the literature. The purpose of this paper is …

It has been over a decade since neural networks were first applied to solve combinatorial
optimization problems. During this period, enthusiasm has been erratic as new approaches …

This paper presents OptNet, a network architecture that integrates optimization problems
(here, specifically in the form of quadratic programs) as individual layers in larger end-to-end …

Computers have undergone tremendous improvements in performance over the last 60
years, but those improvements have significantly slowed down over the last decade, owing …

Portfolio optimization is one of the most important investment strategies in financial markets.
It is practically desirable for investors, especially high-frequency traders, to consider …

Mobile robots tracking a reference trajectory are constrained by the motion limits of their
actuators, which impose the requirement for high autonomy driving capabilities in robots …

A novel class of information-processing systems called cellular neural networks is proposed.
Like neural networks, they are large-scale nonlinear analog circuits that process signals in …

This technical note presents a second-order multi-agent network for distributed optimization
with a sum of convex objective functions subject to bound constraints. In the multi-agent …

From the Publisher: Artificial neural networks can be employed to solve a wide spectrum of
problems in optimization, parallel computing, matrix algebra and signal processing. Taking a …

Neuromorphic photonics has experienced a recent surge of interest over the last few years,
promising orders of magnitude improvements in both speed and energy efficiency over …