A novel differential dynamic gradient descent optimization algorithm for resource allocation and offloading in the COMEC system

Abstract

The multiuser cooperative offloading mobile edge computing (COMEC) system has attracted much attention because it can realize delay-sensitive tasks. However, in the coupling optimization of offloading decision and resource allocation, the existing numerical optimization algorithms are difficult to obtain high-quality optimization solutions. In this paper, we propose a differential dynamic gradient descent (DDGD) optimization algorithm to solve the above optimization problems. DDGD algorithm decomposes the constrained NP-hard optimization problem into two network layers and integrates the constraint function into a larger end-to-end training network. These two-layer networks encode the dependencies and optimization constraints between parameter hidden states, which cannot be captured by a numerical optimization model or a full connection layer neural network. Because the ring learning and self-repeating learning architecture are adopted and the information is stored in the differential dynamics network, the proposed algorithm can achieve better and more intelligent decision-making in searching the solution trajectory without setting accurate parameters in advance and

Chinese Language and Literature Education and Humanistic Literacy Enhancement in Colleges and Universities under the Background of Education Informatization

“…The optimization of deep learning-based text classification models is a non-surge problem, and traditional methods are difficult to guarantee reaching the full-drama optimum. Therefore, we usually use gradient descent-based algorithms to optimize the model [25].…”