How to find the indices of non-zero elements in a matrix

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S. David 2014-6-27

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编辑： S. David 2014-6-30

Hello,

I have an Na-by-Nt matrix which is sparse, i.e.: most elements are zeros. I want to find the indices of the non-zeros elements in the form of (i,j) where i is the row and j is the column.

I have then these two Na-by-1 vector a and Nt-by-1 vector tau. i corresponds to the ith element in a and j the jth element in tau, and I want to find them as well.

How can I do the above in an efficient way without the need for for loops?

Thanks

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Answer 1

Cedric 2014-6-27

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编辑：Cedric 2014-6-27

在 MATLAB Online 中打开

If it is for storing only non-zero elements, MATLAB supports SPARSE matrices. If you need to get row/column indices of non-zero elements of A:

[rId, cId] = find( A ) ;

and if you need values as well:

[rId, cId, val] = find( A ) ;

If you just need values, you can get them simply with

vals = A(A ~= 0) ;

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Cedric 2014-6-27

编辑：Cedric 2014-6-27

在 MATLAB Online 中打开

For data storage/manipulation of matrices which are sparse enough, I suggest that you use MATLAB built-in sparse matrices, and that you avoid building your own, less efficient and less versatile data structure. Most functions from the MATLAB base package do support sparse matrices and will run algorithms optimized for them when they are given sparse matrices as input.

I recommend that you build sparse matrices using calls of the form

S = sparse(i,j,s,m,n) ;

and that you avoid building them dense first and then converting them to sparse with

S = sparse( A )

At this point, I don't understand why you need indices if you want to get all non-zero values, as you can get them with

val = A(A ~= 0) ;

If you have a vector of row indices and a vector of column indices (which are subsets of the sets of all indices), you can get corresponding values by converting the "subscript" indices to linear indices using SUB2IND, and indexing the array with the linear index.

S. David 2014-6-30

在 MATLAB Online 中打开

asp-v1.0.zip

Here is my MATLAB code for A and v in the sparse problem Ax=v:

clear all;
clc;
SNRdB=0:2:12;
SNR=10.^(SNRdB./10);
N=512;
f0=30*10^3;
BW=4*10^3;
df=BW/N;
T=1/df;
Ts=T/N;%Sample time
nsamp=100;%Oversampling
Ta=Ts/nsamp;%Sample time after oversampling
tau=[0 5 7 20].*10^-3;%Path Delays
Np=length(tau);
a=[0 0.001 0.0012 0.0024];
h=[1 0.8 0.7 0.5];
h=h./sqrt(h*h');
CP_ms=30*10^-3;%CP in ms
CP=round(CP_ms/Ta);%CP in samples 
Tg=CP*Ta;
t =( -Tg : Ta : (T+Tg-Ta) )';
fr=(f0+(0:N-1)./T)'*ones(1,N);%Subcarrier indices increase with rows
fc=transpose((f0+(0:N-1)./T)'*ones(1,N));%Subcarrier indices increase with column
H=zeros(N,Np);
for pp=1:Np
    H(:,pp)=H(:,pp)+h(pp).*exp( -1i * 2 * pi * (f0 + (0:N-1)/T)' * tau(pp) );
end
Phi=zeros(N);
for pp=1:Np
    Hc_Col_Incr=transpose(H(:,pp)*ones(1,N));
    Theta=(fc.*kron(1+a(pp),ones(N))-fr);
    Phi=Phi+Hc_Col_Incr.*exp(1i.*pi.*T.*Theta).*sinc(T.*Theta);
end
lambda=2;
Ntau=lambda*N*Tg/T;
NtauL=1:Ntau;
Tau_Tentative_Set=NtauL.*(T/(lambda*N));
aMax=max(a);%5*10^-4;
Doppler_Tentative_Set=linspace(0,aMax,Np);%-aMax:1*10^-4:aMax;
% Na=length(Doppler_Tentative_Set);
s=2.*(rand(N,1)>0.5)-1;
v=Phi*s;
for pp=1:Np
    for tt=1:Ntau
        theta_p=fc.*kron(1+Doppler_Tentative_Set(pp),ones(N))-fr;
        Gamma_p=exp(1i*pi*T.*theta_p).*sinc(T.*theta_p);
        Lambda_p=diag(exp(-1i*2*pi.*(f0+(0:N-1)/T).*Tau_Tentative_Set(tt)));
        if pp==1 && tt==1
            A=Gamma_p*Lambda_p*s;
        else
            A=[A Gamma_p*Lambda_p*s];
        end
    end
end
[xVec,inform] = as_bpdn(A,v);

The function as_bpdn is attached.

Thanks

Cedric 2014-6-30

编辑：Cedric 2014-6-30

在 MATLAB Online 中打开

I meant a simple/minimal numeric example showing which triplets you need to ID in which data structure, because I am not sure that anybody will have time to understand your code/algorithm.

If it is working but too slow, the first thing that comes to my mind is to replace the last double FOR loop with

 A = complex( zeros( numel(s), Np * Ntau )) ;
 colId = 0 ;
 for pp=1:Np
     theta_p=fc.*kron(1+Doppler_Tentative_Set(pp),ones(N))-fr;
     Gamma_p=exp(1i*pi*T.*theta_p).*sinc(T.*theta_p);
     for tt=1:Ntau
         Lambda_p_diag = exp(-1i*2*pi.*(f0+(0:N-1)/T).*Tau_Tentative_Set(tt));
         colId = colId + 1 ;
         A(:,colId) = bsxfun( @mtimes, Gamma_p, Lambda_p_diag ) * s ;
     end
 end

S. David 2014-6-30

编辑：S. David 2014-6-30

It is difficult to give a non real example. However, I think it is not necessary to understand the code/algorithm. At the end, the code will give A, v, and the sparse solution xVec. That is all is needed I guess.

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