# Stable reversed Tartaglia's triangles

Inspired by Minimising the triangle

I am writing a fully tested program to solve the following problem:

A triangle needs a good foundation. Every row in the triangle is derived from the sum of the two values below it. However, there can be no repeated values, if a value shows up more than once the triangle crumbles. Find the base which minimises the value in the top of the triangle satisfying the condition of no duplicates.

Example:

      20
8   12
[3]   5   7
1   2  [3]  4


Here 3 occurs twice, so the triangle is invalid.

My main concern is that my code has time complexity $O(n!)$ in the function stable_triangles, so it is not scalable at all.

"""
Finds Reversed Tartaglia's (or Pascals') triangles where there are
no repeated values.
"""

import doctest
from itertools import permutations

def next_level(level):
"""
Builds the layer above of the triangle by
summing the near pairs of numbers.

>>> next_level([1, 4, 5])
[5, 9]
"""
return [curr + level[index + 1]
for index, curr in enumerate(level[:-1])]

def make_triangle(base):
"""
Builds a full triangle by next_level until the
top has length 1.

>>> make_triangle([1,2,3])
[[1, 2, 3], [3, 5], [8]]
"""
triangle = [base]
while len(triangle[-1]) > 1:
triangle.append(next_level(triangle[-1]))
return triangle

def all_unique(lst):
"""
Are the items all unique?

>>> all_unique([4, 6, 7])
True
>>> all_unique([4, 4, 6, 7])
False
"""
return len(lst) == len(set(lst))

def flatten1(lst):
"""
Flattens one level, shallow.

>>> flatten1([ [1,2], [3,4] ])
[1, 2, 3, 4]
"""
return [i for sublst in lst for i in sublst]

def is_stable(triangle):
"""
A triangle is stable if no number is repeated,
that is: all the numbers are unique.

>>> is_stable([[1,2,3], [3,5], [8]])
False
>>> is_stable([[1,2], [2]])
False
>>> is_stable([[1,3], [4]])
True
"""
return all_unique(flatten1(triangle))

def stable_triangles(base_length):
"""
Yields many stable triangles with the given base length.

>>> flattend_sum = lambda tri: sum(flatten1(tri))
>>> min(stable_triangles(3), key=flattend_sum)
[[2, 1, 4], [3, 5], [8]]
>>> min(stable_triangles(4), key=flattend_sum)
[[2, 3, 1, 6], [5, 4, 7], [9, 11], [20]]
>>> min(stable_triangles(5), key=flattend_sum)
[[6, 4, 1, 2, 7], [10, 5, 3, 9], [15, 8, 12], [23, 20], [43]]
>>> next(stable_triangles(6))
[[1, 2, 4, 7, 5, 8], [3, 6, 11, 12, 13], [9, 17, 23, 25], [26, 40, 48], [66, 88], [154]]

# >>> next(stable_triangles(7)) # This takes about a minute. Comment out for faster testing.
# [[1, 2, 4, 7, 5, 8, 10], [3, 6, 11, 12, 13, 18], [9, 17, 23, 25, 31], [26, 40, 48, 56], [66, 88, 104], [154, 192], [346]]
"""
for base in permutations(range(base_length * 2), base_length):
triangle = make_triangle(list(base))
if is_stable(triangle):
yield triangle

if __name__ == "__main__":
doctest.testmod()
for base_length in range(10):
print(next(stable_triangles(base_length)))

• The program doesn't output the best possible answers for base lengths of 6 and 7, [1, 2, 6, 4, 5, 7, 13] yields 313 Jun 21, 2015 at 15:11
• @spyr03 in fact it does not. It would take a huge time to run through all the combinations, so I just output the first one in that case. Calling min(stable_triangles(7), key=flattend_sum) would yield the correct result in a day's time. Jun 21, 2015 at 15:15
• @Caridorc I don't suppose you're minimizing the wrong thing there (?). The question says you're minimizing the triangle's peak, not the sum of the triangle. Jun 22, 2015 at 9:34

This code is written very idiomatically, so I don't have any style points.

For speed, the first thing to do is use PyPy. That gives a 5x throughput improvement.

Then one should consider your algorithm. A quick run with cProfile gives

   Ordered by: internal time

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
8420076    1.231    0.000    1.231    0.000 p.py:5(<listcomp>)
1414943    0.966    0.000    0.992    0.000 p.py:14(all_unique)
1414943    0.557    0.000    2.462    0.000 p.py:8(make_triangle)
1414943    0.504    0.000    0.504    0.000 p.py:18(<listcomp>)
8420076    0.477    0.000    1.708    0.000 p.py:4(next_level)
8    0.275    0.034    4.347    0.543 p.py:23(stable_triangles)
8421251    0.114    0.000    0.114    0.000 {method 'append' of 'list' objects}
12667944/12667894    0.109    0.000    0.109    0.000 {built-in function len}
1414943    0.077    0.000    1.610    0.000 p.py:20(is_stable)
1414943    0.037    0.000    0.541    0.000 p.py:17(flatten1)
1    0.020    0.020    4.466    4.466 p.py:1(<module>)


This means that next_level is taking a considerable amount of time. I noticed it's better written

[left + right for left, right in zip(level[:-1], level[1:])]


but this doesn't change speed much.

The first useful thing I would consider is interspersing generation and testing to prevent many calls to next_level. This also prevents the need to keep the whole triangle - one only needs the base and current row. This can be done by making triangle yield rows instead.

from itertools import permutations

def next_level(level):
return [left + right for left, right in zip(level[:-1], level[1:])]

def make_triangle(base):
row = base

while row:
yield row
row = next_level(row)

def is_stable(triangle):
seen = set()

for row in triangle:
if not seen.isdisjoint(row) or len(row) != len(set(row)):
return False
seen.update(row)

return True

def stable_triangles(base_length):
for base in permutations(range(base_length * 2), base_length):
if is_stable(make_triangle(base)):
yield make_triangle(base)


This is pretty fast comparatively. Not fast enough, though!

Here's where we get to the really interesting parts.

Consider some unique triangle:

    9
3   6
1   2   4


and the next

     26
9  17
3   6  11
1   2   4   7


It's not a coincidence that one is a superset of another. In fact, is must be so. For any non-trivial unique triangle, there are subsets you can find by dropping the left or right hand side.

So consider building them up in this manner, filtering as early as possible. One problem is that we need a value limit to prevent infinite recursion. Luckily, I can choose the same rule you already have to avoid changing behaviours:

def next_level(level):
return [left + right for left, right in zip(level[:-1], level[1:])]

def make_triangle(base):
row = base

while row:
yield row
row = next_level(row)

def is_stable(triangle):
seen = set()

for row in triangle:
if not seen.isdisjoint(row) or len(row) != len(set(row)):
return False
seen.update(row)

return True

def stable_triangle_bases(base_length, limit):
if base_length < 0:
raise ValueError("Base length must be nonnegative")

if not base_length:
yield ()
return

for base in stable_triangle_bases(base_length - 1, limit):
for i in range(limit):
if is_stable(make_triangle(base + (i,))):
yield base + (i,)

if __name__ == "__main__":
for base_length in range(40):
print(list(next(stable_triangle_bases(base_length, limit=base_length * 2))))


This works really well - up to about length 300 or so, in fact.

I suppose we could try to speed this up more, but that seems beside the point. We want to find the minimum. Well, this isn't totally pointless - we can just run a min with a good key on our result. Unfortunately, though, I haven't managed to get past

                                1001
523     478
309     214     264
195     114     100     164
125      70      44      56     108
79      46      24      20      36      72
48      31      15       9      11      25      47
27      21      10       5       4       7      18      29
14      13       8       2       3       1       6      12      17


with that code.

Further, a limit of base_length * 2 is not optimal. Consider the triangle

                              1000
485     515
273     212     303
171     102     110     193
112      59      43      67     126
73      39      20      23      44      82
46      27      12       8      15      29      53
28      18       9       3       5      10      19      34
17      11       7       2       1       4       6      13      21


!

Raising the limit makes things even slower - an unbounded limit would take forever!

One conceptually easy, but extremely effective, change is to apply a heuristic to the search. This says that if we have found a triangle of cost $k$ and our new path could not give us a cost lower than that, do not try the path.

I originally avoided this because I could not think of a strong enough heuristic. It turns out, though, that it's really simple. Consider this unfinished triangle:

       ??
??  ??
9  ??  ??
3   6  ??  ??
1   2   4   x  ??


We are going to place onto x next, for all values that will fit. Fill in the unknowns on the base with the minimal values that fit, pushing larger numbers to the edges.

In this case, we cannot add 1, 2, 3 or 4, so we fill in a 5. Treat the x as a 0 for now:

       38
19  19
9  10   9
3   6   4   5
1   2   4   x   5


Note that the duplicates here are allowed, as removing them requires knowing the value of x.

Further, consider the contribution of x:

       4x
x  3x
0   x  2x
0   0   x   x
0   0   0   x   0


We can add these two triangles together, giving a top value of

4x + 38


as our heuristic. Now when outputting results, we can ignore any branches where the heuristic brings us above (or equal to) that. (If you want all of the best options, use strict inequality. If you only need one, use non-strict inequality.)

Here is some code to try. It is not particularly pretty, but it works and is pretty fast.

from itertools import chain, count

def pascal_row(row):
val = 1
yield val
for k in range(row):
val *= row - k
val //= k + 1
yield val

def make_triangle(row):
while row:
yield tuple(row)
row = [left + right for left, right in zip(row[:-1], row[1:])]

def heuristic_minimum(base_so_far, length):
used = set().union(*make_triangle(base_so_far))
unused = (x for x in count(1) if x not in used)

factors = pascal_row(length - 1)
raw_cost =  sum(a * b for a, b in zip(base_so_far, factors))
x_factor = next(factors)
raw_cost += sum(a * b for a, b in zip(unused, sorted(factors, reverse=True)))

return x_factor, raw_cost

def is_stable(triangle):
seen = set()

for row in triangle:
if not seen.isdisjoint(row) or len(row) != len(set(row)):
return False
seen.update(row)

return True

def stable_triangle_bases(base_length, upper_bound, full_length):
if base_length < 0:
raise ValueError("Base length must be nonnegative")

if not base_length:
yield ()
return

for base in stable_triangle_bases(base_length - 1, upper_bound, full_length):
x_factor, min_const_cost = heuristic_minimum(base, full_length)

for i in count(1):
cost = i * x_factor + min_const_cost

if cost >= upper_bound.value:
break

if is_stable(make_triangle(base + (i,))):
if base_length == full_length:
upper_bound.value = cost

yield base + (i,)

class UpperBound:
def __init__(self):
self.value = float("inf")

if __name__ == "__main__":
base_length = 10

bases = stable_triangle_bases(
base_length,
UpperBound(),
base_length
)

for result in bases:
print(result)


This will print the improving bound as time progresses.

To top this off, here's a nice big triangle:

                                      4497
2301    2196
1298    1003    1193
778     520     483     710
479     299     221     262     448
296     183     116     105     157     291
181     115      68      48      57     100     191
109      72      43      25      23      34      66     125
65      44      28      15      10      13      21      45      80
38      27      17      11       4       6       7      14      31      49
20      18       9       8       3       1       5       2      12      19      30

• I guess, your final triangle is not optimal, as at least up to size=10, every optimal triangle contains a size-2 triangle in its middle. So there should be 1000 instead of 1003. But I'm not sure, as my code isn't good enough for size=11. Anyway, good job! +++ Damn, I see now that your 4497 indeed is optimal. Jun 26, 2015 at 1:10
• "every optimal triangle contains a size-2 triangle in its middle" → What does this mean? Jun 26, 2015 at 6:46
• I could generate triangles up to size=10 only and for all of them the following holds: Removing all the leftmost and rightmost numbers leads to an optimal triangle of size-2. Jun 26, 2015 at 12:55
• Size 6 has the base (8, 6, 1, 3, 2, 10), which doesn't IIUC. Jun 26, 2015 at 13:04
• You're right... (though there might be another optimal solution containing (4 2 1 7)... or not). Still it has 20 just below the top. Nevermind, just forget it. +++ I guess, you incremental approach beats my alternatives hands down. Jun 26, 2015 at 13:18