# Multistage Graph (Shortest Path)

A Multistage graph is a directed graph in which the nodes can be divided into a set of stages such that all edges are from a stage to next stage only (In other words there is no edge between vertices of same stage and from a vertex of current stage to previous stage).

We are give a multistage graph, a source and a destination, we need to find shortest path from source to destination. By convention, we consider source at stage 1 and destination as last stage.

Following is an example graph we will consider in this article :- ## Recommended: Please try your approach on {IDE} first, before moving on to the solution.

Now there are various strategies we can apply :-

• The Brute force method of finding all possible paths between Source and Destination and then finding the minimum. That’s the WORST possible strategy.
• Dijkstra’s Algorithm of Single Source shortest paths. This method will find shortest paths from source to all other nodes which is not required in this case. So it will take a lot of time and it doesn’t even use the SPECIAL feature that this MULTI-STAGE graph has.
• Simple Greedy Method – At each node, choose the shortest outgoing path. If we apply this approach to the example graph give above we get the solution as 1 + 4 + 18 = 23. But a quick look at the graph will show much shorter paths available than 23. So the greedy method fails !
• The best option is Dynamic Programming. So we need to find Optimal Sub-structure, Recursive Equations and Overlapping Sub-problems.

Optimal Substructure and Recursive Equation :-

We define the notation :- M(x, y) as the minimum cost to T(target node) from Stage x, Node y.

```Shortest distance from stage 1, node 0 to
destination, i.e., 7 is M(1, 0).

// From 0, we can go to 1 or 2 or 3 to
// reach 7.
M(1, 0) = min(1 + M(2, 1),
2 + M(2, 2),
5 + M(2, 3))
```

This means that our problem of 0 —> 7 is now sub-divided into 3 sub-problems :-

```So if we have total 'n' stages and target
as T, then the stopping condition  will be :-
M(n-1, i) = i ---> T + M(n, T) = i ---> T
```

Recursion Tree and Overlapping Sub-Problems:-
So, the hierarchy of M(x, y) evaluations will look something like this :-

```In M(i, j), i is stage number and
j is node number

M(1, 0)
/          |
/           |
M(2, 1)      M(2, 2)        M(2, 3)
/              /              /
M(3, 4)  M(3, 5)  M(3, 4)  M(3, 5) M(3, 6)  M(3, 6)
.         .       .       .          .        .
.         .       .       .          .        .
.         .       .       .          .        .
```

So, here we have drawn a very small part of the Recursion Tree and we can already see Overlapping Sub-Problems. We can largely reduce the number of M(x, y) evaluations using Dynamic Programming.

Implementation details:
The below implementation assumes that nodes are numbered from 0 to N-1 from first stage (source) to last stage (destination). We also assume that the input graph is multistage.

## C++

 `// CPP program to find shortest distance ` `// in a multistage graph. ` `#include ` `using` `namespace` `std; ` ` `  `#define N 8 ` `#define INF INT_MAX ` ` `  `// Returns shortest distance from 0 to ` `// N-1. ` `int` `shortestDist(``int` `graph[N][N]) { ` ` `  `    ``// dist[i] is going to store shortest ` `    ``// distance from node i to node N-1. ` `    ``int` `dist[N]; ` ` `  `    ``dist[N-1] = 0; ` ` `  `    ``// Calculating shortest path for ` `    ``// rest of the nodes ` `    ``for` `(``int` `i = N-2 ; i >= 0 ; i--) ` `    ``{ ` ` `  `        ``// Initialize distance from i to ` `        ``// destination (N-1) ` `        ``dist[i] = INF; ` ` `  `        ``// Check all nodes of next stages ` `        ``// to find shortest distance from ` `        ``// i to N-1. ` `        ``for` `(``int` `j = i ; j < N ; j++) ` `        ``{ ` `            ``// Reject if no edge exists ` `            ``if` `(graph[i][j] == INF) ` `                ``continue``; ` ` `  `            ``// We apply recursive equation to ` `            ``// distance to target through j. ` `            ``// and compare with minimum distance  ` `            ``// so far. ` `            ``dist[i] = min(dist[i], graph[i][j] + ` `                                        ``dist[j]); ` `        ``} ` `    ``} ` ` `  `    ``return` `dist; ` `} ` ` `  `// Driver code ` `int` `main() ` `{ ` `    ``// Graph stored in the form of an ` `    ``// adjacency Matrix ` `    ``int` `graph[N][N] = ` `      ``{{INF, 1, 2, 5, INF, INF, INF, INF}, ` `       ``{INF, INF, INF, INF, 4, 11, INF, INF}, ` `       ``{INF, INF, INF, INF, 9, 5, 16, INF}, ` `       ``{INF, INF, INF, INF, INF, INF, 2, INF}, ` `       ``{INF, INF, INF, INF, INF, INF, INF, 18}, ` `       ``{INF, INF, INF, INF, INF, INF, INF, 13}, ` `       ``{INF, INF, INF, INF, INF, INF, INF, 2}}; ` ` `  `    ``cout << shortestDist(graph); ` `    ``return` `0; ` `} `

## Python3

# Python3 program to find shortest
# distance in a multistage graph.

# Returns shortest distance from
# 0 to N-1.
def shortestDist(graph):
global INF

# dist[i] is going to store shortest
# distance from node i to node N-1.
dist =  * N

dist[N – 1] = 0

# Calculating shortest path
# for rest of the nodes
for i in range(N – 2, -1, -1):

# Initialize distance from
# i to destination (N-1)
dist[i] = INF

# Check all nodes of next stages
# to find shortest distance from
# i to N-1.
for j in range(N):

# Reject if no edge exists
if graph[i][j] == INF:
continue

# We apply recursive equation to
# distance to target through j.
# and compare with minimum
# distance so far.
dist[i] = min(dist[i],
graph[i][j] + dist[j])

return dist

# Driver code
N = 8
INF = 999999999999

# Graph stored in the form of an
# adjacency Matrix
graph = [[INF, 1, 2, 5, INF, INF, INF, INF],
[INF, INF, INF, INF, 4, 11, INF, INF],
[INF, INF, INF, INF, 9, 5, 16, INF],
[INF, INF, INF, INF, INF, INF, 2, INF],
[INF, INF, INF, INF, INF, INF, INF, 18],
[INF, INF, INF, INF, INF, INF, INF, 13],
[INF, INF, INF, INF, INF, INF, INF, 2]]

print(shortestDist(graph))

# This code is contributed by PranchalK

## C#

 `// C# program to find shortest distance  ` `// in a multistage graph. ` `using` `System;  ` `   `  `class` `GFG  ` `{  ` `    ``static` `int` `N = 8;  ` `    ``static` `int` `INF = ``int``.MaxValue;  ` `       `  `    ``// Returns shortest distance from 0 to  ` `    ``// N-1.  ` `    ``public` `static` `int` `shortestDist(``int``[,] graph) {  ` `       `  `        ``// dist[i] is going to store shortest  ` `        ``// distance from node i to node N-1.  ` `        ``int``[] dist = ``new` `int``[N];  ` `       `  `        ``dist[N-1] = 0;  ` `       `  `        ``// Calculating shortest path for  ` `        ``// rest of the nodes  ` `        ``for` `(``int` `i = N-2 ; i >= 0 ; i--)  ` `        ``{  ` `       `  `            ``// Initialize distance from i to  ` `            ``// destination (N-1)  ` `            ``dist[i] = INF;  ` `       `  `            ``// Check all nodes of next stages  ` `            ``// to find shortest distance from  ` `            ``// i to N-1.  ` `            ``for` `(``int` `j = i ; j < N ; j++)  ` `            ``{  ` `                ``// Reject if no edge exists  ` `                ``if` `(graph[i,j] == INF)  ` `                    ``continue``;  ` `       `  `                ``// We apply recursive equation to  ` `                ``// distance to target through j.  ` `                ``// and compare with minimum distance   ` `                ``// so far.  ` `                ``dist[i] = Math.Min(dist[i], graph[i,j] +  ` `                                            ``dist[j]);  ` `            ``}  ` `        ``}  ` `       `  `        ``return` `dist;  ` `    ``}  ` `       `  `    ``// Driver code  ` `    ``static` `void` `Main()  ` `    ``{  ` `        ``// Graph stored in the form of an  ` `        ``// adjacency Matrix  ` `        ``int``[,] graph = ``new` `int``[,]  ` `          ``{{INF, 1, 2, 5, INF, INF, INF, INF},  ` `           ``{INF, INF, INF, INF, 4, 11, INF, INF},  ` `           ``{INF, INF, INF, INF, 9, 5, 16, INF},  ` `           ``{INF, INF, INF, INF, INF, INF, 2, INF},  ` `           ``{INF, INF, INF, INF, INF, INF, INF, 18},  ` `           ``{INF, INF, INF, INF, INF, INF, INF, 13},  ` `           ``{INF, INF, INF, INF, INF, INF, INF, 2}};  ` `       `  `        ``Console.Write(shortestDist(graph)); ` `    ``} ` `    ``//This code is contributed by DrRoot_ ` `} `

Output:

```9
```

Time Complexity : O(n2)

This article is attributed to GeeksforGeeks.org

code

load comments