Rodolphe - 1 month ago 25

Python Question

I have a set of points in a 3D space, from which I need to find the Pareto frontier. Speed of execution is very important here, and time increases very fast as I add points to test.

The set of points looks like this:

`[[0.3296170319979843, 0.0, 0.44472108843537406], [0.3296170319979843,0.0, 0.44472108843537406], [0.32920760896951373, 0.0, 0.4440408163265306], [0.32920760896951373, 0.0, 0.4440408163265306], [0.33815192743764166, 0.0, 0.44356462585034007]]`

Right now, I'm using this algorithm:

`def dominates(row, candidateRow):`

return sum([row[x] >= candidateRow[x] for x in range(len(row))]) == len(row)

def simple_cull(inputPoints, dominates):

paretoPoints = set()

candidateRowNr = 0

dominatedPoints = set()

while True:

candidateRow = inputPoints[candidateRowNr]

inputPoints.remove(candidateRow)

rowNr = 0

nonDominated = True

while len(inputPoints) != 0 and rowNr < len(inputPoints):

row = inputPoints[rowNr]

if dominates(candidateRow, row):

# If it is worse on all features remove the row from the array

inputPoints.remove(row)

dominatedPoints.add(tuple(row))

elif dominates(row, candidateRow):

nonDominated = False

dominatedPoints.add(tuple(candidateRow))

rowNr += 1

else:

rowNr += 1

if nonDominated:

# add the non-dominated point to the Pareto frontier

paretoPoints.add(tuple(candidateRow))

if len(inputPoints) == 0:

break

return paretoPoints, dominatedPoints

Found here: http://code.activestate.com/recipes/578287-multidimensional-pareto-front/

What's the fastest way to find the set of non-dominated solutions in an ensemble of solutions? Or, in short, can Python do better than this algorithm?

Answer

I took a shot at rewriting the same algorithm with a couple of tweaks. I think most of your problems come from `inputPoints.remove(row)`

. This requires searching through the list of points; removing by index would be much more efficient.
I also modified the `dominates`

function to avoid some redundant comparisons. This could be handy in a higher dimension.

```
def dominates(row, rowCandidate):
return all(r >= rc for r, rc in zip(row, rowCandidate))
def cull(pts, dominates):
dominated = []
cleared = []
remaining = pts
while remaining:
candidate = remaining[0]
new_remaining = []
for other in remaining[1:]:
[new_remaining, dominated][dominates(candidate, other)].append(other)
if not any(dominates(other, candidate) for other in new_remaining):
cleared.append(candidate)
else:
dominated.append(candidate)
remaining = new_remaining
return cleared, dominated
```

Source (Stackoverflow)

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