standard_newton.m

function [obj_GD]=standard_newton...
    (XX,YY, no_workers, num_feature, noSamples, num_iter,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
     

     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);
         temp = lambda_logistic*eye(num_feature,num_feature);
         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
        
    
    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
        

        
        
    end   
    
end