I have two matrices (tri1
and tri2
) which represent a Delaunay triangulation. tri1
is the triangulation before inserting a new point, tri2
is the result after adding a new point. Each row has 4 columns. The rows represent tetrahedra.
I would like to calculate a relation between lines from tri1
to tri2
. A result could look like this:
result =
1 1
2 2
3 3
4 4
0 0 % tri1(5, :) was not found in tri2 (a lot more lines could be missing)
6 5
7 6
8 7
9 8
10 9
Currently my source code looks like this:
% sort the arrays
[~, idx1] = sort(tri1(:, 1), 'ascend');
[~, idx2] = sort(tri2(:, 1), 'ascend');
stri1 = tri1(idx1, :);
stri2 = tri2(idx2, :);
result = zeros(size(tri1, 1), 2);
% find old cells in new triangulation
deleted = 0;
for ii = 1:size(tri1, 1)
found = false;
for jj = ii-deleted:size(tri2, 1)
if sum(stri1(ii, :) == stri2(jj, :)) == 4 % hot spot according to the profiler
found = true;
break;
end
if (stri1(ii, 1) < stri2(jj, 1)), break, end;
end
if found == false
deleted = deleted + 1;
else
result(idx1(ii), 1) = idx1(ii);
result(idx1(ii), 2) = idx2(jj);
end
end
The above source code gives me the results that I want, but not fast enough. I am not very experienced with MATLAB, I usually work with C++. My question: How can I speed up the comparison of two rows?
Some additional information (just in case):
- the number of rows in
tri
can grow to about 10000 - this function will be called once per inserted vertex (about 1000)