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pro vyhledávání: '"O'Neill, Michael A."'
Autor:
O'Neill, Michael J.
We develop a Sequential Quadratic Optimization (SQP) algorithm for minimizing a stochastic objective function subject to deterministic equality constraints. The method utilizes two different stepsizes, one which exclusively scales the component of th
Externí odkaz:
http://arxiv.org/abs/2408.16656
Autor:
O'Neill, Michael, Connor, Mark
We present this article as a small gesture in an attempt to counter what appears to be exponentially growing hype around Artificial Intelligence (AI) and its capabilities, and the distraction provided by the associated talk of science-fiction scenari
Externí odkaz:
http://arxiv.org/abs/2307.04821
Autor:
Connor, Mark, O'Neill, Michael
This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts
Externí odkaz:
http://arxiv.org/abs/2305.03851
Publikováno v:
Journal of Accounting Literature, 2023, Vol. 46, Issue 2, pp. 153-169.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JAL-12-2022-0126
A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving a stochastic objective function and deterministic nonlinear equality
Externí odkaz:
http://arxiv.org/abs/2112.14799
One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learnin
Externí odkaz:
http://arxiv.org/abs/2111.14655
A sequential quadratic optimization algorithm is proposed for solving smooth nonlinear equality constrained optimization problems in which the objective function is defined by an expectation of a stochastic function. The algorithmic structure of the
Externí odkaz:
http://arxiv.org/abs/2106.13015
Autor:
O'Neill, Michael, Brabazon, Anthony
We wish to explore the contribution that asocial and social learning might play as a mechanism for self-adaptation in the search for variable-length structures by an evolutionary algorithm. An extremely challenging, yet simple to understand problem l
Externí odkaz:
http://arxiv.org/abs/2104.08239
Autor:
Connor, Mark, O'Neill, Michael
The purpose of this research was to compare the robustness and performance of a local and global optimization algorithm when given the task of fitting the parameters of a common non-linear dose-response model utilized in the field of exercise physiol
Externí odkaz:
http://arxiv.org/abs/2012.09287
Autor:
O'Neill, Michael, Wright, Stephen J.
We describe a line-search algorithm which achieves the best-known worst-case complexity results for problems with a certain "strict saddle" property that has been observed to hold in low-rank matrix optimization problems. Our algorithm is adaptive, i
Externí odkaz:
http://arxiv.org/abs/2006.07925