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newton_zero.m
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function [obj_GD, loss_GD, transmitted_bits]=newton_zero...
(XX,YY, no_workers, num_feature, noSamples, num_iter, obj0, lambda_logistic)
s1=num_feature;
s2=noSamples;
grads=ones(num_feature,no_workers);
hessian = ones(num_feature,num_feature, no_workers);
out_central=zeros(s1,1);
max_iter = num_iter;
for i = 1:max_iter
if i==1
transmitted_bits(i)=num_feature^2*32+num_feature*32;
else
transmitted_bits(i)=transmitted_bits(i-1)+num_feature*32;
end
for ii =1:no_workers
first = (ii-1)*s2+1;
last = first+s2-1;
%grads(:,ii)=XX(first:last,1:num_feature)'*XX(first:last,1:num_feature)*out_central-XX(first:last,1:num_feature)'*YY(first:last);
grads(:,ii)=-(XX(first:last,1:num_feature)'*(YY(first:last)./(1+exp(YY(first:last).*(XX(first:last,1:num_feature)*out_central)))))+lambda_logistic*out_central;
%hessian(:,:,ii)= XX(first:last,1:num_feature)'*XX(first:last,1:num_feature);
if i==1
temp = lambda_logistic*eye(num_feature,num_feature);
%noSamples = last-first+1;
for jj=first:last
temp=temp+YY(jj)^2*XX(jj,:)'*XX(jj,:)*(exp(YY(jj)*XX(jj,:)*out_central)/(1+exp(YY(jj)*XX(jj,:)*out_central))^2);
end
hessian(:,:,ii)=temp;
end
end
out_central=out_central-sum(hessian,3)\sum(grads,2);%inv(sum(hessian,3))*sum(grads,2);
%final_obj = 0;
final_obj =lambda_logistic*0.5*norm(out_central)^2;
for ii =1:no_workers
first = (ii-1)*s2+1;
last = first+s2-1;
%final_obj = final_obj + 0.5*norm(XX(first:last,1:s1)*out_central - YY(first:last))^2;
final_obj = final_obj+sum(log(1+exp(-YY(first:last).*(XX(first:last,1:s1)*out_central))));
end
%i
obj_GD(i)=final_obj;
final_obj
abs(final_obj-obj0)
loss_GD(i)=abs(final_obj-obj0);
end
end