Description

1. Dijkstra’s algorithm: This is not covered in the lectures because it’s the sort of thing many of you have seen before, possibly multiple times. If you haven’t seen it, or need a refresher, look at Chapter 4.4 of [DPV]. We will assume you know the answer to the following questions:
(a) What is the input and output for Dijkstra’s algorithm?
(b) What is the running time of Dijkstra’s algorithm using min-heap (aka priority queue) data structure? (Don’t worry about the Fibonacci heap implementation or running time using it.)
(c) What is the main idea for Dijkstra’s algorithm?
2. [DPV] Problem 3.3 (Topological ordering example)
3. [DPV] Problem 3.4 (SCC algorithm example)
4. [DPV] Problem 3.5 (Reverse of graph)
5. [DPV] Problem 3.8 (Pouring water), if your book has a part (c) you can skip it… or not… this is just practice!
6. [DPV] Problem 7.10 (max-flow = min-cut example)
7. [DPV] Problem 7.17 (bottleneck edges).
8. [DPV] Problem 7.19 (verifying max-flow)
9. For a bipartite graph G = (V1 ∪ V2,E) where |V1| = |V2| = n a perfect matching is a subset S of edges where each vertex is incident exactly 1 edge in S. In other words, it’s a matching of size n. Given a bipartite graph G show how to determine if G has a perfect matching by a reduction to the max-flow problem. So given G define an input to the max-flow problem, and given a max-flow for this input how do you determine if the original graph G has a perfect matching or not? What is the running time of your algorithm?
(For hints see [DPV] Chapter 7.3 (Bipartite matching) and the beginning of Problem 7.24.)
Instructions:
In this class, assume we’re always using min-heap data structure for Dijkstra’s. Report runtime accordingly.
In the algorithm design problems: use the algorithms from class, such as DFS, BFS, Dijkstra’s, connected components, etc., as a black-box subroutine for your algorithm. So say what you are giving as input, then what algorithm you are running, and what’s the output you’re taking from it. Here’s an example:
I take the input graph G, I first find the vertex with largest degree, call it v∗.

I take the complement of the graph G, call it G. Run Dijkstra’s algorithm on G with s = v∗ and then I get the array dist[v] of the shortest path lengths from s to every

other vertex in the graph G. I square each of these distances and return this new array.
We don’t want you to go into the details of these algorithms and tinker with it, just use it as a black-box as showed with Dijkstra’s algorithm above. Make sure to explain your algorithm in words, no pseudocode.
You can use Explore (subroutine of DFS) as one of the black-box algorithms.
Problem 1 [DPV] Problem 3.15 (Computopia)
• Assume that the Town Hall sits at 1 single intersection.
• The mayor’s claim in (b) is the following: If you can get to another intersection, call it u, from the Town Hall intersection, then you can get back to the Town Hall intersection from u.
• Note, linear time means O(n + m) where n = |V | and m = |E|.
Part (a):
Part (b):
Problem 2 Edge on MST
You are given a weighted graph G = (V,E) with positive weights, ci for all i ∈ E. Give a linear time (O(|E| + |V |)) algorithm to decide if an input edge e = (u,v) ∈ E with weight ce is part of some MST of G or not.
You should describe your algorithm in words (a list is okay); no pseudocode.