My first bsxfun solution.
a = [ 7 8 9 7 8 9];
b = 0:10:(10+10*n);
c = bsxfun(@plus,a,b.');
c =
7 8 9 7 8 9
17 18 19 17 18 19
27 28 29 27 28 29
37 38 39 37 38 39
47 48 49 47 48 49
57 58 59 57 58 59
67 68 69 67 68 69
77 78 79 77 78 79
87 88 89 87 88 89
97 98 99 97 98 99
107 108 109 107 108 109
Bit of explanation, though a full and complete introduction to bsxfun can be found in this answer by Divakar.
What happens is that your row array a gets piece-wise added to the column vector b. Thus the first element of a, being 7 in this case, gets added to the column vector b=[10;20;30;...] and becomes the first column of your output matrix c. The second entry of a is summed with the same column vector b and becomes the second column of c. This gets repeated to fill the entire matrix c to a size of numel(b) x numel(a).
This is becoming quite the coding fest. I ran some bench test, running a loop with n=1000 a hundred times and averaged the results. Windows 7, MATLAB R2012a, i5-750 CPU. Actually, the for loop is not even the worst in terms of timing:
bsxfun: 0.00003556 s.
repmat: 0.00048514 s.
cumsum: 0.00015726 s.
for : 0.00033096 s.
Timing revisited on the same system, but with MATLAB R2015a. for is now the slowest, with the others edging towards one another, but bsxfun prevails!
bsxfun: 0.00002030 s.
repmat: 0.00005213 s.
cumsum: 0.00002180 s.
for : 0.00019560 s.
Where I used this for loop implementation:
base = [7 8 9 7 8 9];
A = zeros(1e3,length(base));
for n = 1:1e3;
A(n,:) = base+10*n;
end