gradient_descent

gradient_descent, an Octave code which uses gradient descent to solve a linear least squares (LLS) problem.

Licensing:

The computer code and data files described and made available on this web page are distributed under the MIT license

Languages:

gradient_descent is available in a MATLAB version and an Octave version and a Python version.

Related Data and Programs:

gradient_descent_test

llsq, an Octave code which solves the linear least squares (LLS) problem. It finds the "best" straight line to match data points. More precisely, it finds the quantities "a" and "b" defining the straight line y=a*x+b, which minimizes the root-mean-square (RMS) error to the data.

Source Code:

• gradient_descent_data_fitting.m, uses gradient descent to minimize the norm of the L2 error in a data fitting problem.
• gradient_descent_linear.m, uses gradient descent to minimize the L2 norm of the error in a linear least squares problem.
• gradient_descent_nonlinear.m, uses gradient descent to minimize the L2 norm of a scalar function f(x) of a scalar value x.
• gradient_descent_stochastic.m, uses stochastic gradient descent to minimize the L2 norm of a vector function f(x) of a vector argument x.
• gradient_descent_vector_f.m, uses gradient descent to minimize the L2 norm of a vector function f(x) of a vector argument x.
• gradient_descent_vector_x.m, uses gradient descent to minimize the L2 norm of a scalar function f(x) of a vector argument x.