Both theoretic analysis and simulations confirm the proposed DCEE-NAGD algorithm significantly improves the performance so reduces the autonomous search time.doi:10.1016/j.neucom.2025.129729Guoqiang TanDepartmen
NAG法是一种基于梯度下降法的优化算法,旨在提高梯度下降法的收敛速度。NAG法由俄罗斯数学家尤里·内斯捷罗夫(Yurii Nesterov)于1983年提出,最早用于求解凸优化问题。其核心思想是通过引入动量项来加速梯度下降过程,从而在传统的梯度下降法的基础上加速收敛,特别是在强凸函数的情况下,具有显著的优势。NAG法的一个重要特...
我们提出了一种无约束优化平滑强凸函数(smooth and strongly convex function)的新方法,该方法能够获得 Nesterov 加速梯度下降(Nesterov's accelerated gradient descent)的最佳收敛速度。这一新算法有着简单的几何解释,大致受到椭球法(ellipsoid method)的启发。我们提供了一些数据,证明了该方法可以优于 Nesterov 的加速梯...
作为一个调参狗,每天用着深度学习框架提供的各种优化算法如Momentum、AdaDelta、Adam等,却对其中的原理不甚清楚,这样和一条咸鱼有什么分别!(误)但是我又懒得花太多时间去看每个优化算法的原始论文,幸运的是,网上的大神早就已经帮人总结好了:《An overview of gradient descent optimization algorithms》,看完了这篇文...
tensorflows十五 再探Momentum和Nesterov's accelerated gradient descent 利用自动控制PID概念引入误差微分控制超参数改进NAGD,速度快波动小 神经网络BP-GD算法和自动控制PID算法有类似之处,都是利用误差反馈对问题进行求解,不同的是自动控制调节的是系统的输入,神经网络调节的是系统本身。本文将引入误差微分控制超参数kd_...
June 2015 We propose a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov’s accelerated gradient descent. The new algorithm has a simple geometric interpretation, loosely inspired by the ellip...
For both case studies, the effect of the gradient approximation accuracy (perturbation number) was also investigated. The results indicated that the proposed algorithm is less sensitive to the gradient approximation accuracy than the steepest descent framework. In addition, this study investigated the ...
We propose a new method for unconstrained optimization of a smooth and strongly convex function, which attains the optimal rate of convergence of Nesterov’s accelerated gradient descent. The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method. We provi...
The new algorithm has a simple geometric interpretation, loosely inspired by the ellipsoid method. We provide some numerical evidence that the new method can be superior to Nesterov's accelerated gradient descent.doi:10.48550/arXiv.1506.08187Bubeck, Sébastien...
上述具体详细推导可见:近端梯度下降算法(Proximal Gradient Algorithm),提示:z是变量,z之外的常数项可以去掉或添加,不影响z的改变。 则为了最小化公式(5),即一方面另z 尽可能靠近(x−t∇g(x)) (对g(x),尽可能靠近梯度下降方向),另一方面另h(z) 尽可能小。 则可以定义Proximal operator(近端算子)如下...