% Define environment (functions, gradients etc...) GivenEnv % Define parameters max_iter = 300; % Maximum iterations tol = 1e-4; % Tolerance % Methods tuning amijo_beta = 0.5; % Armijo reduction factor amijo_sigma = 0.1; % Armijo condition constant % Point x0 = (0, 0) % x0 = [0, 0]; % Point x0 = (0, 0) x0s = [0, 0; -1, 1 ; 1, -1]; % Initial points for i = 1:size(x0s, 1) x0 = x0s(i, :); point_str = "[" + x0(1) + ", " + x0(2) + "]"; % Find the best fixed gamma k = zeros(100, 1); j = 1; n = linspace(0.1, 1.5, 100); for g = n gamma_fixed_step = g; [~, ~, k(j)] = newton(fun, grad_fun, hessian_fun, x0, tol, max_iter, 'fixed'); j = j + 1; end plotIterationsOverGamma(n, k, "Iteration for different $\gamma$ values", "Newton_Iter_o_gamma_" + i + ".png"); [~, j] = min(k); gamma_fixed_step = n(j); [x_fixed, f_fixed, kk] = newton(fun, grad_fun, hessian_fun, x0, tol, max_iter, 'fixed'); fprintf('Fixed step: Initial point (%f, %f), steps:%d, Final (x,y)=(%f, %f), f(x,y)=%f\n', x0, kk, x_fixed(end, :), f_fixed(end)); plotPointsOverContour(x_fixed, fun, [-2, 2], [-3, 3], 100, point_str + ": Newton $\gamma$ = " + gamma_fixed_step, "Newton_fixed_" + i + ".png"); % Minimized f [x_minimized, f_minimized, kk] = newton(fun, grad_fun, hessian_fun, x0, tol, max_iter, 'minimized'); fprintf('Minimized f(g): Initial point (%f, %f), steps:%d, Final (x,y)=(%f, %f), f(x,y)=%f\n', x0, kk, x_minimized(end, :), f_minimized(end)); plotPointsOverContour(x_minimized, fun, [-2, 2], [-3, 3], 100, point_str + ": Newton minimized $f(x_k + \gamma_kd_k)$", "Newton_minimized_" + i + ".png"); % Armijo Rule [x_armijo, f_armijo, kk] = newton(fun, grad_fun, hessian_fun, x0, tol, max_iter, 'armijo'); fprintf('Armijo step: Initial point (%f, %f), steps:%d, Final (x,y)=(%f, %f), f(x,y)=%f\n', x0, kk, x_armijo(end, :), f_armijo(end)); plotPointsOverContour(x_armijo, fun, [-2, 2], [-3, 3], 100, point_str + ": Newton Armijo method", "Newton_armijo_" + i + ".png"); end