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Copy pathDDZO_PDA.m
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DDZO_PDA.m
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function [xminuxbar, sq_grad,time] = DDZO_PDA(L_aug, x, n,N,gc,cf,lambda,aalpha, features, labels,bs, iter_num)
fprintf('Starting DDZO_PDA\n');
sq_grad = zeros(iter_num, 1);
xminuxbar = zeros(iter_num, 1);
time = zeros(iter_num, 1);
v = zeros(n*N, iter_num);
eta1 = 1.6;
alpha = 85;
beta = 5;
upd = textprogressbar(iter_num);
u1 = 0.01;
for t = 2:iter_num
tic;
upd(t);
eta = eta1;
u = u1 / t^(2.5); % Parameter for u (delta). 2.45
gradient = zeros(N*n,1);
for ii = 1 : N
std_basis = eye(n);
grad_temp = zeros(n, 1);
for zz = 1:n
for jj=(ii-1)*bs+1:ii*bs
grad_temp = grad_temp + (cf(x((ii-1)*n+1:ii*n,t-1)+u*std_basis(:, zz),lambda,aalpha, features(:,jj), labels(jj),bs, N) - cf(x((ii-1)*n+1:ii*n,t-1),lambda,aalpha, features(:,jj), labels(jj),bs, N)).*std_basis(:, zz)/u;
end
end
gradient((ii-1)*n+1:ii*n) = gradient((ii-1)*n+1:ii*n) + grad_temp;
end
x(:, t) = x(:, t-1) - eta*( alpha*L_aug*x(:, t-1) + beta*v(:, t-1) + gradient );
v(:, t) = v(:, t-1) + eta*beta*L_aug*x(:, t-1);
xs = reshape(x(:,t), [n, N]);
x_avg = sum(xs, 2) / N;
for k = 1:N
xminuxbar(t) = xminuxbar(t)+(norm(xs(:, k)-x_avg))^2;
% fminufstar(t) = fminufstar(t)+loss_func(x_avg, y{k}, a_Re{k}, a_Im{k});
end
temp_grad = zeros(N*n,1);
for ii = 1 : N
for jj=(ii-1)*bs+1:ii*bs
temp_grad((ii-1)*n+1:ii*n) = temp_grad((ii-1)*n+1:ii*n) + gc(x_avg,lambda,aalpha, features(:,jj), labels(jj),bs, N); % This is compute the gradient in each node, batch_size works here.
end
end
g = reshape(temp_grad, [n, N]);
sq_grad(t) = sum(sum(g, 2).^2);
t_temp = toc;
time(t) = time(t - 1) + t_temp;
end
end