Welcome to LinearLeastSquares

LinearLeastSquares, or LLS for short, is a library that makes it easy to formulate and solve least squares optimization problems with linear equality constraints. With LLS, these types of problems can be created using a natural syntax that mirrors standard mathematical notation.

LinearLeastSquares is a software package developed for the course, Introduction to Matrix Methods (EE103), taught by Professor Stephen Boyd at Stanford University. The accompanying text for LinearLeastSquares is Vectors, Matrices, and Least Squares.

For example, the classic problem of finding the least norm solution to an underdetermined system can be easily setup and solved with the following code:

using LinearLeastSquares

# Problem data
p = 20;
n = 30;
C = randn(p, n);
d = randn(p, 1);

# Build the components of the problem
x = Variable(n);
objective = sum_squares(x);
constraint = C * x == d;

# Solve the problem
optimal_value = minimize!(objective, constraint)

This example showcases the Julia implementation of LLS; other implementations include Python.