Zobrazeno 1 - 10
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pro vyhledávání: '"gradient algorithm"'
Publikováno v:
Application Research of Computers / Jisuanji Yingyong Yanjiu. Sep2024, Vol. 41 Issue 9, p2751-2757. 7p.
Autor:
Lepel, Olivier, Barakat, Anas
The widely used expected utility theory has been shown to be empirically inconsistent with human preferences in the psychology and behavioral economy literatures. Cumulative Prospect Theory (CPT) has been developed to fill in this gap and provide a b
Externí odkaz:
http://arxiv.org/abs/2410.02605
This paper presents an Accelerated Preconditioned Proximal Gradient Algorithm (APPGA) for effectively solving a class of Positron Emission Tomography (PET) image reconstruction models with differentiable regularizers. We establish the convergence of
Externí odkaz:
http://arxiv.org/abs/2409.13344
Autor:
Yin, George, Krishnamurthy, Vikram
We analyze the finite sample regret of a decreasing step size stochastic gradient algorithm. We assume correlated noise and use a perturbed Lyapunov function as a systematic approach for the analysis. Finally we analyze the escape time of the iterate
Externí odkaz:
http://arxiv.org/abs/2410.08449
Reinforcement learning with general utilities has recently gained attention thanks to its ability to unify several problems, including imitation learning, pure exploration, and safe RL. However, prior work for solving this general problem in a unifie
Externí odkaz:
http://arxiv.org/abs/2410.04108
Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parameter, $\boldsymbol{\lambda}_g$. An
Externí odkaz:
http://arxiv.org/abs/2410.00848
Autor:
Boyle, Peter A
We introduce a class of efficient multiple right-hand side multigrid algorithm for domain wall fermions. The simultaneous solution for a modest number of right hand sides concurrently allows for a significant reduction in the time spent solving the c
Externí odkaz:
http://arxiv.org/abs/2409.03904
Stochastic decentralized optimization algorithms often suffer from issues such as synchronization overhead and intermittent communication. This paper proposes a $\underline{\rm F}$ully $\underline{\rm S}$tochastic $\underline{\rm P}$rimal $\underline
Externí odkaz:
http://arxiv.org/abs/2410.18774