Gradientedbaserade
Gradientedbaserade, in English gradient-based, refers to methods that use the gradient information of a scalar objective function to guide updates during optimization or learning. The gradient points in the direction of steepest increase, so minimization typically proceeds by stepping in the opposite direction. Gradient-based methods rely on differentiability and access to gradient computations, which can be exact analytically, numerically, or via automatic differentiation.
Common gradient-based optimization methods include plain gradient descent, stochastic gradient descent (SGD) and its variants with
Automatic differentiation enables efficient and accurate gradient computation in complex models, notably deep neural networks where
Applications span machine learning, scientific computing, engineering, and control. Practical issues include ill-conditioning, noisy gradients, and