function [u_hat] = challenge_huber_inpainting(mask, f, D, lambda, epsilon, beta, delta, E_star) % CHALLENGE_HUBER_INPAINTING % % Implement your algorithm to minimize the Huber-inpainting energy % given on the slide with title "Fast optimization challenge" in chapter 2. % % Input parameters: % - mask: N-dimensional vector, is either 0 or 1. % - f: N-dimensional vector, image to be inpainted. % - D: 2N x N-dimensional finite difference matrix. % - lambda: parameter in front of data term % - epsilon: parameter of the huber penalty % - beta: parameter in front of squared term % - delta: stopping tolerance % - E_star: energy of globally optimal solution % % Expected output: % - u_hat: N-dimensional vector, solution to the minimization % problem satisfying (E(u_hat) - E_star) / E_star < delta % !!! TODO: implement your minimization method here !!! N = size(f, 1); u_hat = zeros(N, 1); end