Combinatorial optimization problems are often encountered in real-world applications, including logistics, scheduling and ...

Combinatorial optimization problems are encountered often in various real-world applications, including logistics, scheduling, and network design ...

Solving complex optimization problems is central to many modern technologies, from logistics and financial modeling to chip ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a consequence of global warming. These disasters cause billions of dollars in ...

Are you passionate about developing AI-based and quantum-inspired solutions for the next generation of sustainable energy systems? We are now looking for a fully funded Doctoral Researcher to work on ...

Abstract: This paper proposes a new energy management method for a multi-energy microgrid (MEMG) which supplies both electrical and thermal energies. Based on the transactive energy (TE) concept, the ...

ABSTRACT: The nearly analytic discretization of the frequency-domain wave equation produces large-scale, sparse, and ill-conditioned linear system, which challenge conventional iterative solvers. To ...

This program is an optimized implementation of an analytic continuation method proposed by Andrey S. Mishchenko. A detailed description of the method can be found in ...

Abstract: Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, ...

A global research team led by scientists from China’s Tianjin Renai College has developed a novel stochastic optimization technique for enhanced dispatching and operational efficiency in PV-powered ...