Last time we calculated a solution to the *next greatest number* problem --- unfortunately our algorithm was not an efficient one: evaluating `nextGreater xs`

takes `O(n^2)`

time, as we must evaluate `filter (> x)`

for all `x`

s in `xs`

.

```
nextGreater :: [Int] -> [Maybe Index]
nextGreater = map findGreater . init . tails . indexed
where
findGreater = uncons >=> uncurry (find . snd)
find e = headMay . map fst . filter ((> e) . snd)
```

In this post, we will attempt to derive a solution that matches the `O(n)`

complexity of the imperative solution.

On closer inspection, we note that a lot of repeated computation is being performed on the subproblems --- the computation on a suffix repeats the work in the suffix to follow. Indeed, the use of `tails`

induces an *optimal substructure* on the computation, meaning that we can employ dynamic programming.

If we look at the list from right to left, we can frame it as a dynamic programming problem --- given `[ a : b : c ... ]`

, the next greater element of `a`

is either `b`

, or the next greater element of `b`

if `b <= a`

.

Is this correct?

No, take the case where you have a list `[ 10, 1, 2, 12 ]`

. 1 is not greater than 10, but the next greater element of 1 is 2, which is also smaller than 10. Instead, if `b <= a`

you need to keep discarding the following NGE's until you find one that is greater than `a`

. Which is basically the same solution as before.

Let's consider the problem differently, where instead of using `tails`

and we used `inits`

. If I have a list `[a ,b, c]`

, where `b`

and `c`

are not the NGE's of `a`

, then we have to consider the elements following it, i.e. `d, e...`

until one matches.

This suggests a single scan, linear time solution using a stack to keep track of everything that we have not found the NGE for. Then for each element in the list we. However this will cause us to emit things out of order as we aren't storing things into slots in an array.

First define the action for emitting (i.e. popping things off the stack):

```
emit :: [(Index,Int)] -> (Index, Int) -> ([(Index,Int)] , [(Index,Index)])
emit [] x = ([], [])
emit stk@((i,e):ss) x@(j,e')
| e' < e = (stk, [])
| otherwise = ((i,j) :) <$> emit ss x
```

Then we simply lift this operation over the list with a fold:

```
nextGreaterL :: [Int] -> [(Index,Index)]
nextGreaterL = snd . fold step ([],[]) . indexed
where
step (stk,ret) x = (ret <>) <$> emit stk x
```

This takes linear time in the worst case, and each `step`

takes a constant amortised time.

```
def solve(ls):
xs, size = reduce(lambda (xs, i), x: (xs + [(x, i)], i+1), ls, ([], 0))
answer = map(lambda x: -1, ls)
stack = []
for (x1, i1) in xs:
while len(stack) != 0:
x2, i2 = stack.pop()
if x2 < x1:
answer[i2] = i1
else:
stack.append((x2, i2))
break
stack.append((x1, i1))
return answer
print solve([1,4,10,3,5])
```