Can we sum a series of values,even some of them are "Inf"?If i want to do it,how can i do?

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yang-En Hsiao 2019-3-26

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评论： Torsten 2019-3-27

I get a series answer of optimal problem in several times,and i want to sum of them and average them,however, some of them are "Inf",can i or how to write a code to ignore those "Inf" and sum the others which is not "Inf" ?

Also,if i can sum them when some of them are "Inf",and don't know the amount of value which is "Inf",do we have some method to average them,i mean,ignore the "Inf" and average the others?

The version of matlab is 2015a

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Answer 1

John D'Errico 2019-3-26

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编辑：John D'Errico 2019-3-26

No. You cannot add numbers if some of them are infs, at least not without getting an inf as a result. OR you might even get a NaN, since inf+ -inf will result in a NaN.

sum([1:5,inf,6,7,-inf])
ans =
   NaN

but

sum([1:5,inf,6,7,inf])
ans =
   Inf

You also cannot know that some infs are bigger than other infs, as I think you may have asked.

If you want to form a sum of numbers, excluding infs, this is trivial:

V = [1:5,inf,6,7,-inf];
sum(V(~isinf(V)))
ans =
    28

Also, if you have a reasonably recent MATLAB release, you will find that tools like mean allow you to ignore NaNs in the data as an option. So you could simply convert all infs into NaNs, then use mean with the correct option set. (Or use nanmean in older releases.)

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yang-En Hsiao 2019-3-27

Here is my code,and i found a bug in here if i use the mean(c(~isinf(c)))

bd=2
for l=1:bd
%hat_p_up=P_p;
 hat_p_up=0.0824
%OP4
%declare
K=4;
N=4;
L=5;%distance between RX & TX
xi=10^-4%tolerence between 
nois_var_hk_2pow=0.1*(L^(-2.5))*10;%W,0.1*(L^(-2.5)),if this unit is dbm
w_nois_var_hk_2pow=10^(-3)*10^(0.1*nois_var_hk_2pow)
nois_var_ak_2pow=[1.0000e-10 1.0000e-10 1.0000e-10 1.0000e-10 ];
nois_var_dk_2pow=[1.0000e-08 1.0000e-08 1.0000e-08 1.0000e-08 ];
bar_r=[10 10 10 10]
P_T=10
co = zeros(2,1);
h_1=sqrt(w_nois_var_hk_2pow/2)*(randn(N,1)+1i*randn(N,1));
h_2=sqrt(w_nois_var_hk_2pow/2)*(randn(N,1)+1i*randn(N,1));
h_3=sqrt(w_nois_var_hk_2pow/2)*(randn(N,1)+1i*randn(N,1));
h_4=sqrt(w_nois_var_hk_2pow/2)*(randn(N,1)+1i*randn(N,1));
h_kk=cat(2,h_1 ,h_2 ,h_3, h_4)
 for n=1:4
    h_k{n}=h_kk(1:4 , n);
    n=n+1;
 end
%==========================
cvx_begin
    variable FNNK_up(N,N,K) semidefinite;%c7
    variable rho_k_up(1,1,K) semidefinite;
%==========================
Fkk_up=cat(2,FNNK_up);
    
    up=0
   for o_up=1:4
       Fk_up{o_up}=Fkk_up(1:4,o_up+3*up:4*o_up)
       up=up+1;
   end
   tr_ace_up=0
   for t=1:K
       tr_ace_up=tr_ace_up+trace(Fk_up{t})  
   end
%====================================    
%object function
    minimize( tr_ace_up )
%==================================== 
%Constraint 
subject to 
%c3
rho_k_up<=1;
%===================================================
%c5
       c5_left_hand_up = 0;
        for k = 1:K
            sum_5_up = 0;
            for j = 1:K
                if j ~= k  
                 sum_5_up = sum_5_up +  h_k{k}' * Fk_up{j} * h_k{k};
                end
            end      
            c5_left_hand_up = c5_left_hand_up - sum_5_up+ (h_k{k}' * Fk_up{k} * h_k{k}*inv_pos(bar_r(1)))
            c5_right_hand_up= nois_var_ak_2pow(1)+ ( nois_var_dk_2pow(1)*inv_pos(rho_k_up(k)) )
            real( c5_left_hand_up ) >=   c5_right_hand_up
        end
%===================================================   
%c10
    c10_left_hand_up = 0;           
            sum_10_up = 0;
            for j = 1:K                  
                 sum_10_up= sum_10_up +  h_k{k}' * Fk_up{j} * h_k{k}; 
            end
            c10_left_hand_up = c10_left_hand_up + sum_10_up+nois_var_ak_2pow(1)
            c10_right_hand_up=hat_p_up*inv_pos(1-rho_k_up(k))  
            real(c10_left_hand_up)>=  c10_right_hand_up    
cvx_end  
end
co(bd) = cvx_optval
c_sum=sum(co(~isinf(co)))
c_mean = mean(co(~isinf(co)))

I got two cvx_optval,one is Inf and one is 538.6262,so theoretically,the c_sum should be the same as c_mean,bacause i have only one value which is not inf,but i found that c_sum=538.6262,and c_mean= 269.3131,why is that?

By the way,if you want to run my code ,you have to install the cvx into the matlab first

Torsten 2019-3-27

co is a vector with elements 0 and 538.6262. Taking the mean of this vector gives (0+538.6262)/2 = 269.3131, taking the sum of this vector gives 0+538.6262 = 538.6262.

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