# How to accelerate my code?

Given a 2D $M \times N$ matrix with non-negative values, I would like to find a path going from top to bottom such that the sum of the path is minimum. My function is defined as follow:

findSeam[e_List] := Module[{f, t, p, i, j, k, nrows, ncols},

{nrows, ncols} = Dimensions[e];
f = Table[0, {i, nrows}, {j, ncols}];
t = Table[0, {i, nrows}, {j, ncols}];

For [j = 1, j <= ncols, j++,
f[[1, j]] = e[[1, j]];
t[[1, j]] = 0;
];

For [i = 2, i <= nrows, i++,
For [j = 1, j <= ncols, j++,
If [j == 1, k = j, k = j - 1];

If [f[[i-1, j]] < f[[i-1, k]], k = j];
If [j < ncols && f[[i-1, j+1]] < f[[i-1, k]], k = j + 1];

f[[i, j]] = e[[i, j]] + f[[i-1, k]];
t[[i, j]] = k - j;
];
];

p = Table[1, {i, nrows}];
For [j = 2, j <= ncols, j++,
i = p[[-1]];
If [f[[nrows, i]] > f[[nrows, j]],
p[[-1]] = j;
];
];
For [i = nrows - 1, i >= 1, i--,
j = p[[i+1]];
p[[i]] = j + t[[i+1, j]];
];

p
];


The computation complexity is only $O(MN)$. However, when I apply this function to a $900 \times 600$ matrix, it takes about 6 seconds to finish the computation.

Is my coding style wrong? Can my code be optimized such that it runs more quickly? Thank you.

• This reminds me of Problem #81 of project euler. If you code in this style, why not use C/C++? Besides, try compiling your program. I think both the ways can boost its speed. – vapor Mar 22 '15 at 14:16
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• – xyz Mar 22 '15 at 14:31
• Can you supply us with either your list for e or a function to generate the list so we can replicate your results and use them as a baseline? – Jagra Mar 22 '15 at 16:57
• My function to generate e is e = Norm[#]& /@ #& /@ ImageData@ImageConvolve[img, {{1,1,1},{1,-8,1},{1,1,1}}], where img is a 900x600 image. – Purboo Mar 23 '15 at 1:48

Here's my functional variant of your code:

findSeam2[e_List] :=
Module[{f = FoldList[MinFilter[#1, 1] + #2 &, First[e], Rest[e]]},
Reverse@
FoldList[#1 +
First@Ordering[#2[[Max[1, #1 - 1] ;;
Min[Length[#2], #1 + 1]]]] - 1 - If[#1 == 1, 0, 1] &,
First@Ordering[Last[f], 1], Reverse@Most[f]]];


And my test case (inspired by seam carving).

img = ExampleData[{"TestImage", "Lena"}];
AbsoluteTiming[seam = findSeam[data];]
Show[img, Graphics[{Red, Line[Flatten /@ MapIndexed[List, seam]]}]]
AbsoluteTiming[TimeConstrained[seam2 = findSeam2[data];, 5]]
Show[img, Graphics[{Blue, Line[Flatten /@ MapIndexed[List, seam2]]}]]
seam == seam2 On my computer the timings are 2.4 and 0.07 seconds, respectively.

Note that by Compileing your function, the timing is even better:

findSeam3 =
Compile[{{e, _Real, 2}},
Module[ ...
f = Table[0., {i, nrows}, {j, ncols}];
...
], CompilationTarget -> "C", RuntimeOptions -> "Speed"]


I only changed the initializer for f to use 0., so that the compiler is expecting floating-point values. The timing for this test case is only 0.01 seconds!

• That's nice. +1 – ciao Mar 22 '15 at 18:41
• If anyone knows of a better way to write #2[[Max[1, #1 - 1] ;; Min[Length[#2], #1 + 1]]], let me know... I sometimes wish there was a function like Take or Part that automatically truncated out-of-range numbers. – 2012rcampion Mar 22 '15 at 18:50
• Thank you. I think I need to learn "Compile" first. – Purboo Mar 23 '15 at 1:41
• Sometimes it is not easy to write code using functions like Apply, Map and Thread. In this case, is "Compile" the only way to accelerate the speed? – Purboo Mar 23 '15 at 1:45
• @2012rcampion it isn't shorter, but there's Clip, e.g. Span @@ Clip[{#1 - 1, #1 + 1}, {1, Length@#2}], which reads a bit cleaner. – rcollyer Mar 23 '15 at 12:42