Global optimization of signomial geometric programming problems

TL;DR: In this article, a joint BS assignment, sub-carrier and power allocation algorithm is proposed to maximize the network throughput, while satisfying the minimum required rate of each slice, under the assumption that each user at each transmission instance can connect to no more than one BS.

TL;DR: In this article, the authors considered sustainability issues and default risks in the context of joint inventory control and trade credit financing for non-Instantaneous Deteriorating Items (NIDIs) in a supplier-retailer-customer system, in which the supplier offers an upstream full trade credit to the retailer and the retailer, in turn, provides a downstream partial trade credits to her/his customers Demand rate is sensitive to the amount of carbon emissions, the length of the credit period suggested by the retailer to its customers and selling price.

TL;DR: This paper develops parallel successive learning (PSL), which expands the FedL architecture along three dimensions, and sheds light on the notion of cold vs. warmed up models, and model inertia in distributed machine learning.

TL;DR: This tutorial paper collects together in one place the basic background material needed to do GP modeling, and shows how to recognize functions and problems compatible with GP, and how to approximate functions or data in a formcompatible with GP.

TL;DR: Inner approximation algorithms have had two major roles in the mathematical programming literature: their first role was in the construction of algorithms for the decomposition of large-scale mathematical programs, such as in the Dantzig-Wolfe decomposition principle and recently they have been used in the creation of algorithms that locate Kuhn-Tucker solutions to nonconvex programs.

TL;DR: This work presents a systematic method of distributed algorithms for power control that is geometric-programming-based and shows that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimized problems in the form of geometric programming.

TL;DR: The Problems of Quadratic Programming and Semidefinite Programming, a Treatise on Nonlinear Systems of Equations, and some of the Problems of Generalized Geometric Programming.