# Greedy Algorithms Minimum Spanning Trees and Dynamic Programming Problem Set #1 Quiz Answer

## Greedy Algorithms Minimum Spanning Trees and Dynamic Programming Problem Set #1 Quiz Answer

In this article i am gone to share Coursera Course Greedy Algorithms Minimum Spanning Trees and Dynamic Programming Problem Set #1 Quiz Answer with you..

#### Problem Set #1

Question 1)

We are given as input a set of n requests (e.g., for the use of an auditorium), with a known start time si and finish time ti for each request i. Assume that all start and finish times are distinct. Two requests conflict if they overlap in time — if one of them starts between the start and finish times of the other. Our goal is to select a maximum-cardinality subset of the given requests that contains no conflicts. (For example, given three requests consuming the intervals [0,3], [2,5], and [4,7], we want to return the first and third requests. We aim to design a greedy algorithm for this problem with the following form: At each iteration we select a new request i, including it in the solution-so-far and deleting from future consideration all requests that conflict with i.

Which of the following greedy rules is guaranteed to always compute an optimal solution?

• At each iteration, pick the remaining request with the fewest number of conflicts with other remaining requests (breaking ties arbitrarily).
• At each iteration, pick the remaining request with the earliest start time.
• At each iteration, pick the remaining request with the earliest finish time.
• At each iteration, pick the remaining request which requires the least time i.e., has the smallest value of ti – si) (breaking ties arbitrarily).

Question 2)

We are given as input a set of n jobs, where job j has a processing time pj; and a deadline dj. Recall the definition of completion times Cj from the video lectures. Given a schedule (i.e., an ordering of the jobs), we define the lateness lj  of job j as the amount of time Cj – dj after its deadline that the job completes, or as O if Cj ≤ dj. Our goal is to minimize the maximum lateness, maxj  lj.

Which of the following greedy rules produces an ordering that minimizes the maximum lateness? You can assume that all processing times and deadlines are distinct.

• Schedule the requests in increasing order of deadline di
• Schedule the requests in increasing order of processing time pi
• Schedule the requests in increasing order of the product d; p;
• None of the other answers are correct.

Question 3)

In this problem you are given as input a graph T = (V, E) that is a tree (that is, T is undirected, connected, and acyclic). A perfect matching of T is a subset F ⊂ E of edges such that every vertex vV is the endpoint of exactly one edge of F. Equivalently, F matches each vertex of T with exactly one other vertex of T. For example, a path graph has a perfect matching if and only if it has an even number of vertices.

Consider the following two algorithms that attempt to decide whether or not a given tree has a perfect matching. The degree of a vertex in a graph is the number of edges incident to it. (The two algorithms differ only in the choice of v in line 5.)

Algorithm A:

while I has at least one vertex:

If I has no edges:

halt and output “T has no perfect matching.”

Else:

Let v be a vertex of I with maximum degree.

Choose an arbitrary edge e incident to v.

Delete e and its two endpoints from T.

[end of while loop]

Halt and output “T has a perfect matching.”

Algorithm B:

while T has at least one vertex:

If I has no edges:

halt and output “T has no perfect matching.”

Else:

Let v be a vertex of T with minimum non-zero degree.

Choose an arbitrary edge e incident to v.

Delete e and its two endpoints from T.

[end of while loop]

Halt and output “T has a perfect matching.”

Is either algorithm correct?

• Neither algorithm always correctly determines whether or not a given tree graph has a perfect matching.
• Both algorithms always correctly determine whether or not a given tree graph has a perfect matching.
• Algorithm B always correctly determines whether or not a given tree graph has a perfect matching; algorithm A does not.
• Algorithm A always correctly determines whether or not a given tree graph has a perfect matching; algorithm B does not.

Question 4)

Consider an undirected graph G = (V, E) where every edge e ∈ E has a given cost  ce. Assume that all edge costs are positive and distinct. Let T be a minimum spanning tree of G and P a shortest path from the vertex s to the vertex t. Now suppose that the cost of every edge e of G is increased by 1 and becomes  ce +1. Call this new graph G‘. Which of the following is true about G’?

• T may not be a minimum spanning tree but P is always a shortest set path.
• T must be a minimum spanning tree but P may not be a shortest s-t path.
• T is always a minimum spanning tree and P is always a shortest s-t path.
• T may not be a minimum spanning tree and P may not be a shortest s-t path.

Question 5)

Suppose T is a minimum spanning tree of the connected graph G. Let H be a connected induced subgraph of G. (l.e.,H is obtained from G by taking some subset S ⊆ V of vertices, and taking all edges of E that have both endpoints in S. Also, assume H is connected.) Which of the following is true about the edges of T that lie in H? You can assume that edge costs are distinct, if you wish. [Choose the strongest true statement.]

• For every G and H, these edges are contained in some minimum spanning tree of H
• For every G and H, these edges form a minimum spanning tree of H
• For every G and H, these edges form a spanning tree (but not necessary minimum-cost) of H
• For every G and H and spanning tree TH of H, at least one of these edges is missing from TH