It is an optimization algorithm.
It is technically referred to as a first-order optimization algorithm as it explicitly makes use of the first-order derivative of the target objective function.
- First-order methods rely on gradient information to help direct the search for a minimum …
— Page 69, Algorithms for Optimization, 2019.
The first-order derivative, or simply the “derivative,” is the rate of change or slope of the target function at a specific point, e.g. for a specific input.
If the target function takes multiple input variables, it is referred to as a multivariate function and the input variables can be thought of as a vector. In turn, the derivative of a multivariate target function may also be taken as a vector and is referred to generally as the gradient.
- Gradient: First-order derivative for a multivariate objective function.
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.
The gradient descent algorithm requires a target function that is being optimized and the derivative function for the objective function. The target function f() returns a score for a given set of inputs, and the derivative function f’() gives the derivative of the target function for a given set of inputs.
The gradient descent algorithm requires a starting point (x) in the problem, such as a randomly selected point in the input space.
The derivative is then calculated and a step is taken in the input space that is expected to result in a downhill movement in the target function, assuming we are minimizing the target function.
A downhill movement is made by first calculating how far to move in the input space, calculated as the step size (called alpha or the learning rate) multiplied by the gradient. This is then subtracted from the current point, ensuring we move against the gradient, or down the target function.
- x(t) = x(t-1) – step_size * f’(x(t-1))
The steeper the objective function at a given point, the larger the magnitude of the gradient and, in turn, the larger the step taken in the search space. The size of the step taken is scaled using a step size hyperparameter.
- Step Size (alpha): Hyperparameter that controls how far to move in the search space against the gradient each iteration of the algorithm.
If the step size is too small, the movement in the search space will be small and the search will take a long time. If the step size is too large, the search may bounce around the search space and skip over the optima.