Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function...【吴恩达机器学习学习笔记03】Gradient Descent 一、问题综述 我们上一节已经定义了代价函数J,现在我们下面讲讨论如何找到J的最小值,梯度下降(Gradient Descent)广泛应用于机器学习的众多领域。 首先是问题...
On extremely ill-conditioned problems L-BFGS algorithm degenerates to the steepest descent method. Hence good preconditioner is needed to remedy this. Nonlinear CG retains its key properties independently of problem condition number (although convergence speed decreases on ill-conditioned problems). method...
本文旨在给出Nesterov加速算法之所以能对常规一阶算法加速的一种algebraic解释,这种观点来自于将此种方法intepret成gradient descent(primal view)和mirror descent(dual view)的线性耦合(linear coupling)。这种观点是由 @Zeyuan AllenZhu 和Lorenzo Orecchia在14年严格提出(见下面文章链接)。 Allen-Zhu, Zeyuan, and ...
Gradient descent (GD) algorithm is the widely used optimisation method in training machine learning and deep learning models. In this paper, based on GD, Polyak's momentum (PM), and Nesterov accelerated gradient (NAG), we give the convergence of the algorithms from an initial value to the ...
Below are some challenges regarding gradient descent algorithm in general as well as its variants — mainly batch and mini-batch: Gradient descent is a first-order optimization algorithm, which means it doesn’t take into account the second derivatives of the cost function. However, the curvature...
1)gradient descent algorithm梯度下降法 1.Comparison between GA andgradient descent algorithmin parameter optimization of UPFC fuzzy damping controller;基于遗传算法的UPFC模糊阻尼控制器参数优化及与梯度下降法的比较 2.A few or all of the parameters of the controller are adjusted by using thegradient desce...
Computer Science - Computer Science and Game TheoryGradient descent is an important class of iterative algorithms for minimizing convex functions. Classically, gradient descent has been a sequential and synchronous process. Distributed and asynchronous variants of gradient descent have been studied since ...
We propose a new stochastic gradient descent algorithm based on nested variance reduction. Compared with conventional stochastic variance reduced gradient (SVRG) algorithm that uses two reference points to construct a semi-stochastic gradient with diminishing variance in each epoch, our algorithm uses K+...
Stochastic gradient descent (SGD) has been a go-to algorithm for nonconvex stochastic optimization problems arising in machine learning. Its theory however often requires a strong framework to guarantee convergence properties. We hereby present a full scope convergence study of biased nonconvex SGD, ...
algorithm, rather than by the architecture alone, as evidenced by their ability to memorize pure noise19. Many potential implicit constraints have been proposed to explain why large neural networks work well on unseen data (i.e. generalize)20,21,22,23. One prominent theory is gradient descent ...