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代写算法设计

CSC 445 Homework 3 

代写算法设计 The questions are drawn from the material in class, and in Chapters 9 and 15 of the text, on finding the kth-smallest element,

The questions are drawn from the material in class, and in Chapters 9 and 15 of the text, on finding the kth-smallest element, and the dynamic programming technique.

The homework is worth a total of 100 points. When point breakdowns are not given for the parts of a problem, each part has equal weight.

For general algorithm design questions, (i) present the ideas behind your algorithm in prose and pictures, (ii) argue that your algorithm is correct, and (iii) show your analysis that the algorithm meets the given time bound. Pseudocode is not required, but may aid in your analysis.

 

For questions that ask you to design a dynamic programming algorithm, be sure to use the four-part framework:  代写算法设计

(1) characterize the recursive structure of an optimal solution,

(2) derive a recurrence equation for the value of an optimal solution,

(3) evaluate the recurrence bottom-up in a table, and

(4) recover an optimal solution from the table of solution values.

You will be graded on each part. Be sure to analyze the time for Parts (3) and (4) of your algorithm.

Remember to (a) start each problem on a new page, and (b) put your answers in the correct order. If you can’t solve a problem, state this, and write only what you know to be correct. Neatness and conciseness counts.

 

(1) (Finding elements near the median) (10 points)  代写算法设计

Given an unsorted array A of n distinct numbers and an integer k, where 1 k n, design an algorithm that finds the k numbers in A that are closest in value to the median of A in Θ(n) time. (Note: Finding the numbers that are closest in value to the median has no relationship in general with how these numbers are ordered in the unsorted array A.)

 

(2) (Finding quantiles) (20 points)

For a set S of n numbers and an integer k, where 1 k n, the kth-quantiles of S are k1 elements from S whose ranks in S divide the sorted set into k groups that are of equal size (to within one unit).

Given an unsorted array A of n distinct numbers, design an algorithm that finds the kth-quantiles of A in O(n log k) time.

(Note: As an illustration, the 4-quantiles of a set of scores are the values that define the 25-, 50-, and 75-percentile cutoffs. Similarly, the 10-quantiles of a set are the values that define the 10-, 20-, 30-, …., and 90-percentile cutoffs.)

 

(3) (Answering dynamic kth-smallest queries) (bonus) (10 points)  代写算法设计

Given a set of n numbers, the kth-smallest element can be found in Θ(n) time using the algorithm we learned in class. Suppose we have a dynamic set S of numbers that changes over time, and we want to be able to efficiently support the operations of

(a) inserting a new number into set S, and

(b) finding the kth-smallest element in the current set S, for any k.

We could support both operations by representing S as an unsorted array A. Inserting a new element would take just Θ(1) time. Finding the kth-smallest element, however, would take Θ(n) time. If there are many kth-smallest operations executed on set S, performing all of them will take a large amount of total time. We might like to speed up the time to find the kth-smallest, at a slight increase in the time to insert an element.Design an algorithm that (a) supports inserting an element into S in O(log n) time, and (b) supports finding the kth-smallest element of S in O(log n) time as well.

(Hint: Consider modifying a balanced binary search tree data structure.)

 

代写算法设计
代写算法设计

 

(4) (Longest increasing subsequence) (20 points)  

Given a string S of numbers, an increasing subsequence of S is any subsequence T of S such that the numbers in T, read left-to-right across T, are strictly increasing. For example, if S = (3, 1, 6, 2, 5, 4), an increasing subsequence of S is T = (1, 2, 4).

Design a dynamic programming algorithm that finds the longest increasing subsequence of a string S of length n in Θ(n2 ) time.

(Note: Do not solve this problem by reducing it to the longest common subsequence problem. Instead design a dynamic programming algorithm from first principles.)

 

(5) (Editing strings) (30 points)  代写算法设计

Given two strings A[1 : m] and B[1 : n], the edit distance between A and B is the minimum cost of a script that edits A into B. A script is a series of edit operations, each edit operation has a non-negative cost, and the cost of a script is the sum of the costs of its operations.

The allowed edit operations in a script are:

  • copy, which leaves a character unchanged, and has cost 0,
  • substitute, which replaces a character a with another character b, and has cost csub,
  • insert, which adds a character a into a string, and has cost cins,
  • delete, which removes a character a from a string, and has cost cdel, and
  • transpose, which replaces two adjacent characters ab in a string by the characters ba, and has cost ctra.

Design a dynamic programming algorithm to compute the edit distance between A and B and recover the corresponding edit script in Θ(mn) time. You may assume that an optimal script never edits a given character more than once. The costs of operations are part of the input to your algorithm.

(Hint: Order the operations in an edit script so they occur left-to-right across string A, and then examine the possible ways in which an optimal script could end.)

 

(6) (Discrete knapsack) (20 points)  代写算法设计

In the discrete knapsack problem, the input is a collection of n items with associated weights w1, w2, . . . , wn and values v1, v2, . . . , vn, and a capacity k. Item i has weight wi and value vi  . All weights wi and the capacity k are positive integers. The output is a subset S of the items {1, 2, . . . , n}, called a knapsack, such that the total weight of all the items in knapsack S is at most k, and the total value of all the items in S is maximum. In other words, a solution to the discrete knapsack problem is an optimal knapsack S of items that does not exceed the weight capacity k while having the greatest possible value.

Design a dynamic programming algorithm that solves the discrete knapsack problem in Θ(nk) time.

(Hint: Examine the items in the order 1, 2, . . . , n, and consider knapsacks of all possible capacities.)

Note that Problem (3) is a bonus question, and is not required.

 

代写算法设计
代写算法设计

 

 

 

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