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.

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 ...

Discover how Markov Analysis predicts future states from current data, understand its strengths and weaknesses, and explore its application in finance and business.

Robust stochastic optimisation methods seek decision rules that perform reliably under both inherent randomness and ambiguity in probability models. Combining classical stochastic programming—where ...

Foundational optimization algorithms are the core driving force behind deep learning, evolving from early stochastic gradient descent (SGD) to the widely adopted Adam family. However, as the scale of ...

Abstract: This paper introduces a new stochastic optimization method based on the regularized Fisher information matrix (FIM), named SOFIM, which can efficiently utilize the FIM to approximate the ...

ABSTRACT: Accurate measurement of time-varying systematic risk exposures is essential for robust financial risk management. Conventional asset pricing models, such as the Fama-French three-factor ...

This study presents an optimization method for arranging lattice radiotherapy (LRT) targets to enhance the contrast between peak and valley doses, aiming to improve the treatment effectiveness and ...