Python Implementations of proximal GD, Accelerated proximal GD and ADMM for solving lasso regression
minimize f(x) + g(x)
Proximal gradient descent: minimize g(x) using proximal operator and performance gradient updates on f(x), which has convergence rate of O(1/k).
Accelerated proximal gradient method: include a momentum term to avoid overshooting with faster convergence rate of O(1/k^2).
ADMM: Treats two functions, the objective and constraint, separately. The goal is to minimize f(x)+g(x) subject to (x = z). The u is the running sum of the errors(x - z).
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