In the convex-concave setting, the single-timescale gradient descent ascent (GDA) algorithm is widely used in applications and has been shown to have strong convergence guarantees. In more general settings, however, it can fail to converge. Our contribution is to design TTGDA algorithms that ...
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the cu...
在这里明确了梯度下降是“找到使函数值最小的那个自变量X”,换句话说,在常见的的分类问题中,就算找到使损失函数(Loss Function)最小的那个参数 θ 。这是梯度下降,找到全局最小值(当然大概率是局部最小值) 在强化学习中,我们没有确切的损失函数,我们无法让损失最小,代替的目标是最大化奖励函数(Reward Function)...
Gradient Descent Ascent in Min-Max Stackelberg GamesDenizalp GoktasBrown UniversityComputer ScienceProvidence, Rhode Island, USAdenizalp_goktas@brown.eduAmy GreenwaldBrown UniversityComputer ScienceProvidence, Rhode Island, USAamy_greenwald@brown.eduABSTRACTMin-max optimization problems (i.e., min-max games...
Gradient ascent works in the same manner as gradient descent, with one difference. The task it fulfills isn’t minimization, but rather maximization of some function. The reason for the difference is that, at times, we may want to reach the maximum, not the minimum of some function; this...
Gradient Descentis the most common optimization algorithm inmachine learninganddeep learning. It is a first-order optimization algorithm. This means it only takes into account the first derivative when performing the updates on the parameters. On each iteration, we update the parameters in the opposi...
To find the local minimum using gradient descent, steps proportional to the negative of the gradient of the function at the current point are taken. If taken in the positive direction, the algorithm finds local maximum and this process is called as Gradient Ascent....
最速上升,最速下降和梯度法Steepest Ascent, Steepest Descent, and Gradient Methods 热度: on the convergence of decentralized gradient descent:论分散梯度下降的收敛性 热度: Stochastic gradient descent on Riemannian manifolds:黎曼流形上的随机梯度下降 热度: 相关推荐 Hogwild!: A Lock-Free Approach...
The derivative or the gradient points in the direction of the steepest ascent of the target function for a specific input. Gradient descent refers to a minimization optimization algorithm that follows the negative of the gradient downhill of the target function to locate the minimum of the function...
4 Gradient-ascent algorithm(REINFORCE) 梯度上升算法最大化目标函数J(\theta): \theta_{t+1} = \theta_t + \alpha \nabla_\theta J(\theta) \\ = \theta_t + \alpha \mathbb E[\nabla_\theta ln \pi(A|S, \theta_t)q_\pi(S,A)] \tag{15}实际通过SGD替代: ...