matlab：矢量化4D矩阵和

Question

I need to perform in MATLAB the following calculation:

where w and v are vectors with N elements and A is a four-dimensional matrix (N^4 elements). This can be achieved by the following pedantic code:

N=10;
A=rand(N,N,N,N);
v=rand(N,1);
w=zeros(N,1);

for pp=1:N
  for ll=1:N
    for mm=1:N
      for nn=1:N
        w(pp)=w(pp)+A(pp,ll,mm,nn)*v(ll)*v(mm)*conj(v(nn));
      end
    end
  end
end

which is extemely slow. Is there any way to vectorize this kind of sum in MATLAB?

Answer 1

Approach #1

With few reshape 's and matrix multiplication -

A1 = reshape(A,N^3,N)*conj(v)
A2 = reshape(A1,N^2,N)*v
w = reshape(A2,N,N)*v

---

Approach #2

With one bsxfun , reshape and matrix-multiplication -

A1 = reshape(A,N^3,N)*conj(v)
vm = bsxfun(@times,v,v.')
w = reshape(A1,N,N^2)*vm(:)

---

Benchmarking

This section compares runtimes for the two approaches listed in this post, first tested approach in Shai's post and original approach listed in the question.

Benchmarking Code

N=100;
A=rand(N,N,N,N);
v=rand(N,1);

disp('----------------------------------- With Original Approach')
tic
%// .... Code from the original post   ...//
toc

disp('----------------------------------- With Shai Approach #1')
tic
s4 = sum( bsxfun( @times, A, permute( conj(v), [4 3 2 1] ) ), 4 ); 
s3 = sum( bsxfun( @times, s4, permute( v, [3 2 1] ) ), 3 );
w2 = s3*v; 
toc

disp('----------------------------------- With Divakar Approach #1')
tic
A1 = reshape(A,N^3,N)*conj(v);
A2 = reshape(A1,N^2,N)*v;
w3 = reshape(A2,N,N)*v;
toc

disp('----------------------------------- With Divakar Approach #2')
tic
A1 = reshape(A,N^3,N)*conj(v);
vm = bsxfun(@times,v,v.');
w4 = reshape(A1,N,N^2)*vm(:);
toc

Runtime Results

----------------------------------- With Original Approach
Elapsed time is 4.604767 seconds.
----------------------------------- With Shai Approach #1
Elapsed time is 0.334667 seconds.
----------------------------------- With Divakar Approach #1
Elapsed time is 0.071905 seconds.
----------------------------------- With Divakar Approach #2
Elapsed time is 0.058877 seconds.

Conclusions

The second approach in this post seems to be giving about 80x speedup over the original approach.

Answer 2

You can try using bsxfun .

Assuming v is an N -by-1 column vectors (otherwise, permutations should be modified a little bit).

% sum over n (4th dim)
s4 = sum( bsxfun( @times, A, permute( conj(v), [4 3 2 1] ) ), 4 ); 

Now the interim result is only N -by- N -by- N .

% sum over m (3rd dim)
s3 = sum( bsxfun( @times, s4, permute( v, [3 2 1] ) ), 3 )

Continuing to the last sum

% sum over l (2nd dim)
w = s3*v; 

---

_{Coming to think about it, have you considered using dot in its multidim version? I did not test it, but it should work (maybe some minor corrections).}

s4 = dot( A, permute( conj(v), [4 3 2 1] ), 4 );
s3 = dot( s4, permute( v, [3 2 1] ), 3 );
w = s3*v;