CS计算机代考程序代写 algorithm CS570

CS570
Analysis of Algorithms Fall 2010
Exam I
Name: _____________________ Student ID: _________________
____Monday ____Friday ____DEN
Maximum
Received
Problem 1
20
Problem 2
15
Problem 3
15
Problem 4
15
Problem 5
15
Problem 6
20
Total
100
2 hr exam
Close book and notes
If a description to an algorithm is required please limit your description to within 150 words, anything beyond 150 words will not be considered.

1) 20pts
Mark the following statements as TRUE or FALSE. No need to provide any justification.
[ TRUE ]
The number of spanning trees in a fully connected graph with n vertices goes up exponentially with respect to n .
[ FALSE ]
BFS can be used to find the shortest path between any two nodes in a weighted graph.
[ FALSE ]
DFS can be used to find the shortest path between any two nodes in a non-weighted graph.
[ TRUE ]
While there are different algorithms to find a minimum spanning tree of an undirected connected weighted graph G, all of these algorithms produce the same result for a given graph with unique edge costs.
[ TRUE ]
If T(n) is Θ(f(n)), then T(n) is both O(f(n)) and Ω(f(n)).
[ TRUE ]
The array [20 15 18 7 9 5 12 3 6 2] forms a max-heap.
[ TRUE ]
Suppose that in an instance of the original Stable Marriage problem with n couples, there is a man M who is first on every woman’s list and a woman W who is first on every man’s list. If the Gale-Shapley algorithm is run on this instance, then M and W will be paired with each other.
[ TRUE ]
The complexity of the recursion given by T(n) = 4T(n/2) + cn2 , for some positive
constant c, is O(n2 logn). [ FALSE ]
Consider the interval scheduling problem. A greedy algorithm, which is designed to

always select the available request that starts the earliest, returns an optimal set A.
[ FALSE ]
Any divide and conquer algorithm will run in best case Ω(n log n ) time because the height of the recursion tree is at least log n.

2) 15pts
You are given the below binomial heap. Show all your work as you answer the questions below.
a- Insert a new node with key value 5. Show the resulting tree and intermediate steps if any. Is the resulting heap also a binary heap and/or a Fibonacci heap?
This is also a Fibonacci heap, but not binary heap.

b- Analyze the complexity of your insertion algorithm.
O(logn)
c- Now decrease the key of node 7 to 1. Is the minimum-heap property violated? If so, rearrange the heap. Show the resulting tree and intermediate steps if any.
When key value is changed to 1, min-heap property is violated since 1 is the smallest key value and it has to be at the root node. The rearrangement of key values results in the following heap.
d- Analyze the complexity of the operation in C. O(logn)

3) 15pts
A polygon is convex if all of its internal angles are less than 180◦ (and none of the edges cross each other).We represent a convex polygon as an array V[1…n] where each element of the array represents a vertex of the polygon in the form of a coordinate pair (x, y). We are told that V[1] is the vertex with the minimum x coordinate and that the vertices V[1…n] are ordered counterclockwise. You may also assume that the x coordinates of the vertices are all distinct, as are the y coordinates of the vertices.
a- Give a divide and conquer algorithm to find the vertex with the maximum x coordinate in O(log n) time.
Note that for each 1 ≤ i < n either V[i] < V[i + 1] or V[i] > V[i + 1] (Such an array is called a unimodal array). The main idea is to distinguish these two cases:
1. if V[i] < V[i + 1], then the maximum element of V[1..n] occurs in A[i + 1..n]. 2. In a similar way, if V[i] > V[i +1], then the maximum element of V[1..n] occurs in V[1..i].
This leads to the following divide and conquer solution (note its resemblance to binary search):
1 2 3 4 5 6 7 8
a, b ← 1, n while a < b domid←⌊(a+b)/2⌋ if V[mid] < V[mid + 1] then a ← mid + 1 if V[mid] > V[mid + 1]
return V[a]
then b ← mid
The precondition is that we are given a unimodal array V[1..n]. The postcondition is that V[a] is the maximum element of V[1..n]. For the loop we propose the invariant “The maximum element of V[1..n] is in V[a..b] and a ≤ b”.
When the loop completes, a ≥ b (since the loop condition failed) and a ≤ b (by the loop invariant). Therefore a = b, and by the first part of the loop invariant the maximum element of V[1..n] is equal to V[a].
We use induction to prove the correctness of the invariant. Initially, a = 1 and b = n, so, the invariant trivially holds. Suppose that the invariant holds at the start of the loop. Then, we know that the maximum element of V[1..n] is in V[a..b]. Notice that V[a..b] is unimodal as well. If V[mid] < V[mid + 1], then the maximum element of V[a..b] occurs in V[mid+1..b] by case 1. Hence, after a ← mid+1 and b remains unchanged in line 4, the maximum element is again in V[a..b]. The other case is symmetric. To complete the proof, we need to show that the second part of the invariant a ≤ b is also true. At the start of the loop a < b. Therefore, a ≤ ⌊(a + b)/2⌋ < b. This means that a ≤ mid < b such that after line 4 or line 5 in which a and b get updated a ≤ b holds once more. The divide and conquer approach leads to a running time of T(n) = T(n/2)+ Θ(1) =Θ(log n). b- Give a divide and conquer algorithm to find the vertex with the maximum y coordinate in O(log n) time. After finding the vertex V[max] with the maximum x-coordinate, notice that the y-coordinates in V[max], V[max + 1], . . . , V[n − 1], V[n], V[1] form a unimodal array and the maximum y- coordinate of V[1..n] lies in this array. Thus the divide and conquer solution in part a can be used to find the vertex with the maximum y-coordinate. The total running time is Θ(log n). 4) 15pts You are given a weighted directed graph G =(V,E,w) and the shortest path distances δ(s, u) from a source vertex s to every other vertex in G. However, you are not given π(u) (the predecessor pointers). With this information, give an algorithm to find a shortest path from s to a given vertex t in O(|V| + |E|) time. Start at u. Of the edges that point to u, at least one of them will come from a vertex v that satisfies δ(s, v)+w(v, u)= δ(s, u). Such a v is on the shortest path. Recursively find the shortest path from s to v. This algorithm hits every vertex and edge at most once, for a running time of O(|V| + |E|). 5) 15pts Suppose you are choosing between the following three algorithms:  Algorithm A solves problems of size n by dividing them into five subproblems of half the size, recursively solving each subproblem, and then combining the solutions in linear time.  Algorithm B solves problems of size n by recursively solving two subproblems of size n – 1 and then combining the solutions in constant time.  Algorithm C solves problems of size n by dividing them into nine subproblems of size n/3, recursively solving each subproblem, and then combining the solutions in O(n2) time. What are the running times of each of these algorithms (in big-O notation), and which would you choose? Algorithm A solves problems by dividing them into five subproblems of half the size, recursively solving each subproblem, and then combining the solutions in linear time. T(n) = 5 T(n/2) + c n Applying master theorm, a=2, b=5, f(n)=c n, degree(f(n))=1 Since log2 5 > 1, T(n) = O(nloga b) = O(nlog2 5)
Algorithm B solves problems of size n by recursively solving two subproblems of size n- 1 and then combining the solutions in constant time.
T(n)=2T(n-1)+c=22 T(n-2)+2c+c=(2n -1)c
T(n) = O(2n)
Algorithm C solves problems of size n by dividing them into nine subproblems of size n/3, recursively solving each subproblems and then combining the solution in O(n2) time.
T(n) = 9 T(n/3) + c n2
Applying master theorm, a=3, b=9, f(n)= c n2, degree(f(n))=2 Since log3 9 = 2, T(n) = O(n2 log n)
From above three algorithms, we can see that time complexity of the third algorithm is best. Thus, we will choose algorithm C.

6) 20pts
a- Suppose we are given an instance of the Shortest Path problem with source vertex
s on a directed graph G. Assume that all edges costs are positive and distinct. Let P be a minimum cost path from s to t. Now suppose that we replace each edge cost ce by its square root, ce1/2, thereby creating a new instance of the problem with the same graph but different costs.
Prove or disprove: P still a minimum-cost s – t path for this new instance.
The statement can be disproved by giving a counterexample as follows.
G=(V, E); V={s, a, t}; E={(s, a), (a, t), (s, t)};
cost((s, a))=9; cost((a, t))=16; cost((s, t))=36.
It is obvious that the minimum-cost s – t path is s – a – t.
By replacing each edge cost ce by its square. root, ce1/2, the costs become: cost((s, a))=3; cost((a, t))=4; cost((s, t))=6.
Now the the minimum-cost s – t path is s – t, not s – a – t anymore.
b- SupposewearegivenaninstanceoftheMinimumSpanningTreeproblemonan undirected graph G. Assume that all edges costs are positive and distinct. Let T be an MST in G. Now suppose that we replace each edge cost ce by its square root, ce1/2, thereby creating a new instance of the problem (G’) with the same graph but different costs.
Prove or disprove: T is still an MST in G’.
The statement is true due to that replacing each edge cost ce by its square root, ce1/2 does not change the order of the cost, i.e. for positive real numbers a and b, if a is greater than b, a1/2 is greater than b1/2.