Answer:
Explanation:
The maximum weighted independent collection of vertices in a linear chain graph is a straightforward algorithm whereby dynamic programming comes in handy.
Provided a linear chain graph G = (V, E, W), where V is a collection of vertices, E is a set of edges margins, and W is a weight feature function applied to each verex. Our goal is to find an independent collection of vertices in a linear chain graph with the highest total weight of vertices in that set.
We'll use dynamic programming to do this, with L[k] being the full weighted independent collection of vertices spanning from vertex 1 \to vertex k.
If we add vertex k+1 at vertex k+1, we cannot include vertex k, and thus L[k+1] would either be equivalent to L[k] when vertex k+1 is not being used, or L[k+1] = L[k-1] + W[k+1] when vertex k+1 is included.
As a result, the dynamic programming algorithm technique can be applied in the following way.
ALGO(V, W, n) // V is a linearly ordered series of n vertices with such a weight feature W
As a result, using dynamic programming, we can resolve the problem in O(n) only.
This is an example of a time-saving dynamic programming application.