Gradient Descent
An iterative first-order optimization algorithm for finding local minima of a differentiable scalar objective function by taking steps proportional to the negative gradient.
Governing formula
x_(k+1) = x_k - α ∇f(x_k)
SI unit
Iterative Optimization Method
In depth
Step size is controlled by learning rate parameter α. Used across computational physics and machine learning to minimize loss energy landscapes.
Examples in the real world
Finding ground state energy configurations in molecular modeling and training artificial neural networks.