Shouldn't this be using a backtracking algorithm? - c++

Question

I am solving some questions on LeetCode. One of the questions is:
Given a m x n grid filled with non-negative numbers, find a path from top left to bottom right which minimizes the sum of all numbers along its path.You can only move either down or right at any point in time.
The editorial as well as the solutions posted all use dynamic programming. One of the most upvoted solution is as follows:
class Solution {
public:
int minPathSum(vector<vector<int>>& grid) {
int m = grid.size();
int n = grid[0].size();
vector<vector<int> > sum(m, vector<int>(n, grid[0][0]));
for (int i = 1; i < m; i++)
sum[i][0] = sum[i - 1][0] + grid[i][0];
for (int j = 1; j < n; j++)
sum[0][j] = sum[0][j - 1] + grid[0][j];
for (int i = 1; i < m; i++)
for (int j = 1; j < n; j++)
sum[i][j] = min(sum[i - 1][j], sum[i][j - 1]) + grid[i][j];
return sum[m - 1][n - 1];
}
};
My question is simple: shouldn't this be solved using backtracking? Suppose the input matrix is something like:
[
[1,2,500]
[100,500,500]
[1,3,4]
]
My doubt is because in DP, the solutions to subproblems are a part of the global solution (optimal substructure). However, as can be seen above, when we make a local choice of choosing 2 out of (2,100), we might be wrong, since the future paths might be too expensive (all numbers surrounding 2 are 500s). So, how is using dynamic programming justified in this case?
To summarize:
Shouldn't we use backtracking since we might have to retract our path if we have made an incorrect choice previously (looking at local maxima)?
How is this a dynamic programming question?
P.S.: The above solution definitely runs.

Answer 1

The example you illustrated above shows that a greedy solution to the problem will not necessarily produce an optimal solution, and you're absolutely right about that.
However, the DP solution to this problem doesn't quite use this strategy. The idea behind the DP solution is to compute, for each location, the cost of the shortest path ending at that location. In the course of solving the overall problem, the DP algorithm will end up computing the length of some shortest paths that pass through the 2 in your grid, but it won't necessarily use those intermediate shortest paths when determining the overall shortest path to return. Try tracing through the above code on your example - do you see how it computes and then doesn't end up using those other path options?

Answer 2

Shouldn't we use backtracking since we might have to retract our path if we have made an incorrect choice previously (looking at local maxima)?
In a real-world scenario, there will be quite a few factors that will determine which algorithm will be better suited to solve this problem.
This DP solution is alright in the sense that it will give you the best performance/memory usage when handling worst-case scenarios.
Any backtracking/dijkstra/A* algorithm will need to maintain a full matrix as well as a list of open nodes. This DP solution just assumes every node will end up being visited, so it can ditch the open node list and just maintain the costs buffer.
By assuming every node will be visited, it also gets rid of the "which node do I open next" part of the algorithm.
So if optimal worst-case scenario performance is what we are looking for, then this algorithm is actually going to be very hard to beat. But wether that's what we want or not is a different matter.
How is this a dynamic programming question?
This is only a dynamic programming question in the sense that there exists a dynamic programming solution for it. But by no means is DP the only way to tackle it.
Edit: Before I get dunked on, yes there are more memory-efficient solutions, but at very high CPU costs in the worst-case scenarios.

Answer 3

For your input
[
[ 1, 2, 500]
[100, 500, 500]
[ 1, 3, 4]
]
sum array results to
[
[ 1, 3, 503]
[101, 503, 1003]
[102, 105, 109]
]
And we can even retrace shortest path:
109, 105, 102, 101, 1
Algorithm doesn't check each path, but use the property that it can take previous optimum path to compute current cost:
sum[i][j] = min(sum[i - 1][j], // take better path between previous horizontal
sum[i][j - 1]) // or previous vertical
+ grid[i][j]; // current cost

Answer 4

Backtracking, in itself, doesn't fit this problem particularly well.
Backtracking works well for problems like eight queens, where a proposed solution either works, or it doesn't. We try a possible route to a solution, and if it fails, we backtrack and try another possible route, until we find one that works.
In this case, however, every possible route gets us from the beginning to the end. We can't just try different possibilities until we find one that works. Instead, we have to basically try every route from beginning to end, until one find the one that works the best (the lowest weight, in this case).
Now, it's certainly true that with backtracking and pruning, we could (perhaps) improve our approach to this solution, to at least some degree. In particular, let's assume you did a search that started by looking downward (if possible) and then to the side. In this case, with the input you gave its first attempt would end up being the optimal route.
The question is whether it can recognize that, and prune some branches of the tree without traversing them entirely. The answer is that yes, it can. To do that, it keeps track of the best route it's found so far, and based upon that, it can reject entire sub-trees. In this case its first route gives a total weight of 109. Then it tries to the right of the first node, which is a 2, for a total weight of 3 so far. That's smaller than 109, so it proceeds. From there, it looks downward and gets to the 500. That gives a weight of 503, so without doing any further looking, it knows no route from there can be suitable, so it stops and prunes off all the branches that start from that 500. Then it tries rightward from the 2 and finds another 500. This lets it prune that entire branch as well. So, in these cases, it never looks at the third 500, or the 3 and 4 at all--just by looking at the 500 nodes, we can determine that those can't possibly yield an optimal solution.
Whether that's really an improvement on the DP strategy largely comes down to a question of what operations cost how much. For the task at hand, it probably doesn't make much difference either way. If, however, your input matrix was a lot larger, it might. For example, we might have a large input stored in tiles. With a DP solution, we evaluate all the possibilities, so we always load all the tiles. With a tree-trimming approach, we might be able to completely avoid loading some tiles at all, because the routes including those tiles have already been eliminated.