This study guide includes topics covered over the entire semester.
The final will be cumulative. This study guide is intended to be
comprehensive, but not guaranteed to be complete.
In addition to using this study guide, you should:
- Review your lecture notes
- Review lecture videos on Panopto
- Look over assigned readings from the textbook
- Review previous homeworks,
- Review previous tests, and
- Review lab assignments
You should be able to define or explain the following terms:
- Stable Matching a.k.a. Stable Marriage
- Asymptotic Notation (big-Oh, big-Omega, big-Theta)
- induction
- directed, undirected graphs
- breadth-first search, depth-first search (BFS/DFS)
- bipartite graph
- cycle
- connected component
- topological ordering
- spanning tree
- Minimum Spanning Tree (MST)
- Kruskal's/Prim's Algorithms
- Greedy Algorithm
- stays-ahead method
- exchange argument
- Divide and Conquer
- Recurrence Relation
- Recursion Tree
- Substitution/Partial Substitution
- Dynamic Programming
- memoization
- tabulation
- Network Flow
- Residual Graph
- Ford-Fulkerson Algorithm
- minimum cut
- computational complexity
- intractability
- decision problem
- optimization problem
- complexity classes (P, NP, NP-Complete)
- Cook-Levin Theorem
- NP-Complete problems (SAT, VertexCover, IndependentSet, SetCover,
3-Coloring, 3SAT, ...)
- polynomial-time reduction
- polynomial-time verifier
- Approximation Algorithm
- approximation ratio
- minimization problem, maximization problem
- pricing method
Practice problems
Note: The problems below should take much more than three hours
to complete. They are not indended to represent a sample final exam.
Consider them good practice and good preparation for the final.
- Divide and Conquer. (Kleinberg and Tardos 5.2) Recall the
problem of finding the number of inversions. As in the text, we
are given a sequence of n distinct numbers
a1,a2,...,an, and we define an inversion to
be a pair i< j such that ai > aj.
However, one might feel like this measure is too sensitive. Call
a pair a significant inversion if i < j and
ai > 2aj. Give an O(n log(n)) algoirthm to
count the number of significent inversions between two orderings.
- Recurrence Relations. Solve the following recurrence relation.
T(n) = 2T(n/2) + 2n^2,
T(1) = 3
- Dynamic Programming. In the Subset-Sum problem, you
are given n items {1, ..., n}. Each item has a nonnegative integer
weight wi. You are also given an integer weight
threshold W. For a subset of elements S, define w(S) to be the
sum of wi, taken over all i in S. Your goal is to output
the set S that maximizes w(S), subject to w(S) <= W.
Design and analyze an algorithm to solve the
Subset-Sum problem. Your algorithm should run in O(nW) time.
Note:To get full credit, you only need to define the table
your dynamic program will use, show how to use it to
solve Subset-Sum, and show how to compute each table entry
recursively, using smaller table entries. For example, if you needed
to solve the Steel-Rod Problem, the following solution is sufficient
for full credit:
- Let R[0...n] be a one-dimenstional table, such that R[k] is the
maximum revenue obtainable from a k foot rod.
- We wish to output R[n].
- R[0] = 0, and for k>0, R[k] = max {P[j] + R[k-j]}, where the maximum is taken over all 1<=j<=n.
Of course, showing any intuition or work will maximize your chances of getting partial credit.
- Intractability. Consider the following decision-version of
Subset-Sum, which we'll call SSum. In this version, you're
given n items {1,..., n} with item weights wi, and a weight
threshold W, and you must output YES iff there exists a subset
whose item weights total exactly W; i.e., if there is S such that w(S)
= W.
- Show that SSum is NP-Complete.
- Show how to solve SSum using your solution for Subset-Sum.
- Your algorithm for Subset-Sum runs in O(nW) time. Why does
this not give you a polynomial-time algorithm for SSum??
Note: This problem is more work than is reasonable for a three
hour exam. If you can complete and understand this problem, you
should be well-prepared for any NP-Complete problems that might appear
on the final exam.
- Approximation Algorithms. (Kleinberg and Tardos 11.10)
Suppose you are given an n by n grid graph G. Associated with each
node v is a weight w(v), which is a nonnegative integer. You may
assume that the weights of all nodes are distinct. Your goal is to
choose an independent set S of nodes of the grid so that the
sum of the weights of the nodes in S is as large as possible.
Consider the following greedy algorithm for this problem:
- Start with S equal to the empty set
- While some node remains in G
- Pick the heaviest remaining node v.
- Add v to S
- Delete v and all its neighbors from G
- Return S
First, let S be the independent set returned by this algorithm, and
let T be some other independent set in G. Show that for each node v
in T, either v is in S, or v is adjacent to some v' in S with w(v') >=
w(v).
Show that this algorithm returns an independent set with total
weight at least 1/4 of the maximum total weight of an independent set.