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 ...

In this work, we address a question that has attracted intense interest in recent years: whether machine learning-assisted algorithms can genuinely outperform classical approaches in challenging ...

Kylie Kelce says she and Jason Kelce have name in mind for a son We have to get rid of her! Inside Althorp, Princess Diana’s 500-year-old ancestral home US Treasury informed banks that it may ...

Abstract: Vehicle scheduling and dispatching are core optimization problems in large-scale vehicle fleet systems, directly influencing service efficiency, operating cost, and resource utilization.

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...

Optimization on quantum hardware is required in many applications, including Hamiltonian simulation to quantum machine learning; this entails interesting problems that must be addressed both for noisy ...

Metaheuristic optimisation methods constitute a class of algorithmic strategies inspired by natural processes, designed to tackle complex and often non-convex optimisation problems. When embedded ...

Abstract: This paper presents a comprehensive survey of various prompt optimization methods, systematically analyzing and comparing their effectiveness across different experimental settings. We ...