I am getting Memory limit exceeded for this problem C. Dijkstra? But why I can not understand this. My Solution
№ | Пользователь | Рейтинг |
---|---|---|
1 | tourist | 3985 |
2 | jiangly | 3814 |
3 | jqdai0815 | 3682 |
4 | Benq | 3529 |
5 | orzdevinwang | 3526 |
6 | ksun48 | 3517 |
7 | Radewoosh | 3410 |
8 | hos.lyric | 3399 |
9 | ecnerwala | 3392 |
9 | Um_nik | 3392 |
Страны | Города | Организации | Всё → |
№ | Пользователь | Вклад |
---|---|---|
1 | cry | 169 |
2 | maomao90 | 162 |
2 | Um_nik | 162 |
4 | atcoder_official | 161 |
5 | djm03178 | 158 |
6 | -is-this-fft- | 157 |
7 | adamant | 155 |
8 | Dominater069 | 154 |
8 | awoo | 154 |
10 | luogu_official | 150 |
I am getting Memory limit exceeded for this problem C. Dijkstra? But why I can not understand this. My Solution
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The size of queue is getting very large during execution you can either use set like this solution and clear the entry for v line number 29 this will restrict the size of set to at max O(N) http://mirror.codeforces.com/contest/20/submission/43596615
or like this solution you can free some memory by clearing the vectors you are not going to use. http://mirror.codeforces.com/contest/20/submission/43596568
But I can not understand how the set (is it work like priority queue ? )work in here please can you explain it .
the purpose of priority queue is to extract the minimum element. In set the 1st element is the smallest element. While set provide an option to erase a element while the same operation is not supported in priority queue. priority queue is implemented as Heap while set is implemented as red black tree(Binary search tree)
Ok that's great I understood now thank you .
Actually, set has higher overhead compared to priority queue due to additional book-keeping such as balancing. In this case, blue__legend was able to achieve a much better runtime and memory limit because of the fact that we can efficiently search for and remove irrelevant nodes from the set, whereas we can't do so in a priority queue.
Anyway, I think that your main problem is that you didn't terminate your loop, in your dijkstra shortest path implementation, when your destination has been visited. As such, a lot of irrelevant nodes might be stored in the priority queue and cause the MLE to occur.