The statement for this problem can be found here.

In order to solve this problem in a reasonable time you should:

- Never repeat cases that will yield the same results.
- The first solution that fits is the correct one.
- Efficiently advance through the quest.

Let’s take each point separately.

### Not repeating cases

To achieve this is important to notice that whenever you are multiplying 2 numbers the leftmost numbers of the results will only be affected by the *n* leftmost digits of the second argument where *n* equals to the number of digits of the first number plus 1.

So we should be collecting those digits, and before processing a possible multiplicand we check that its first *n* digits were not previously processed.

### The first solution that fits is the correct one

This condition implies that the possible answers are processed in increasing order independently of how they are generated.

I made it possible by storing the possible answers in a min-heap, sorted by the length of the multiplier and then for the digits of the multiplier.

In each iteration I pop the first element, check if that is the solution and then if its not I push each of the possible solutions that can be diverted from that multiplier.

### Efficiently advance through the quest

All the cases that are processed are based on previous possible solutions. Doing the multiplication each time is expensive.
To gain processing time, I store in the tuple that goes into the min-heap the result of the multiplication for that multiplier.

When its processed and it is not the answer, instead of calculating each multiplication for the deriving cases I just add to the current multiplication the *new digit * number being process * 10 ** (size of current multiplier - 1)* which is a lot less expensive.

### Final Solution

Putting together everything, I coded a Python program that solves the problem, the file is problem303.py:

```
import heapq
LIMIT = 10000
mult_digit = {}
for i in range(10):
mult_digit[i] = {}
for j in range(10):
if (i * j) % 10 not in mult_digit[i]:
mult_digit[i][(i * j) % 10] = []
mult_digit[i][(i * j) % 10].append(j)
def find_mult(i):
str_i = str(i)
heap = []
for r in range(3):
heap.extend((2, [j], i * j) for j in mult_digit[int(str_i[-1])].get(r, []))
heapq.heapify(heap)
used_set = set()
while True:
next_affected, list_n, parcial_mult = heapq.heappop(heap)
if list_n[0] != 0 and max(str(parcial_mult)) < '3':
return int(''.join(map(str, list_n)))
if tuple(list_n[:len(str_i)+1]) in used_set:
continue
used_set.add(tuple(list_n[:len(str_i)+1]))
str_parcial_mult = str(parcial_mult)
try:
s = int(str_parcial_mult[-next_affected])
except IndexError:
s = 0
for d in range(3):
d -= s
d %= 10
for mult in mult_digit[int(str_i[-1])].get(d, []):
new_list_n = [mult] + list_n
heapq.heappush(heap, (next_affected + 1, new_list_n, parcial_mult + mult * i * (10 ** (next_affected - 1))))
if __name__ == '__main__':
result = sum(find_mult(i) for i in range(1, LIMIT + 1))
print("The result is:", result)
```