You are given a convex polygon P on n vertices in the plane (specified by their x and y coordinates). A triangulation of P is a collection of n-3diagonals of such that no two diagonals intersect (except possibly at their endpoints). Notice that a triangulation splits the polygon’s interior into n-2 disjoint triangles. The cost of a triangulation is the sum of the lengths of the diagonals in it. Give an efficient algorithm for finding a triangulation of minimum cost. (Hint: Label the vertices of P by 1,....,n, starting from an arbitrary vertex and walking clockwise. For 1i<jn, let the subproblem A(i,j)denote the minimum cost triangulation of the polygon spanned by vertices i,i+1,...,j.).

Short Answer

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Convex Polygon: A convex polygon is a close figure whose each interior angle is less than 180°This means that no diagonal can be made which passes outside the boundary of polygon.

Step by step solution

01

Defining the Recursive Equation of Minimum Cost Triangulation

We will make our recursive equation in such a way that no diagonal cross eachother. For numbering, we will follow clockwise direction.

If Ai,jdenote minimum cost triangulation, where i,jare different vertices, Then i,i=0as we cannot make diagonal using same vertice.

Thus, Recursive Equation is:

localid="1657271470951" Ai,j=0minikjAi,k+Ak,j+di,k+dk,j0;ifi=j

Now it is important to define the length of diagonal(d). It is given as:

di,j=xi-xj2+yi-yj220;otherwise;ifj-i2

02

Implementing Algorithm

The algorithm would be as follows:

fori=0tonAi,j=0form=1ton-1fori=1ton-mj=i+mminikjAi,j+Ak,j+di,k+dk,j

returnA1,n

03

Analyse the Algorithm

Now, the outer for loop takes0ntime to compute each of the entry ofAi,j

The two nested loop takes 0n2time. So the effective time complexity will be0n3.

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Most popular questions from this chapter

Optimal binary search trees. Suppose we know the frequency with which keywords occur in programs of a certain language, for instance:

begin5%do40%else8%end4%

if10%then10%while23%

We want to organize them in a binary search tree, so that the keyword in the root is alphabetically bigger than all the keywords in the left subtree and smaller than all the keywords in the right subtree (and this holds for all nodes). Figure 6.12 has a nicely-balanced example on the left. In this case, when a keyword is being looked up, the number of comparisons needed is at most three: for instance, in finding “while”, only the three nodes “end”, “then”, and “while” get examined. But since we know the frequency 196 Algorithms with which keywords are accessed, we can use an even more fine-tuned cost function, the average number of comparisons to look up a word. For the search tree on the left, it is

cost=1(0.04)+2(0.40+0.10)+3(0.05+0.08+0.10+0.23)=2.42

By this measure, the best search tree is the one on the right, which has a cost of Give an efficient algorithm for the following task. Input: n words (in sorted order); frequencies of these words: p1,p2,...,pn.

Output: The binary search tree of lowest cost (defined above as the expected number of comparisons in looking up a word).

Figure 6.12 Two binary search trees for the keywords of a programming language.

Time and space complexity of dynamic programming. Our dynamic programming algorithm for computing the edit distance between strings of length m and n creates a table of size n×mand therefore needs O (mn) time and space. In practice, it will run out of space long before it runs out of time. How can this space requirement be reduced?

  1. Show that if we just want to compute the value of the edit distance (rather than the optimal sequence of edits), then only O(n) space is needed, because only a small portion of the table needs to be maintained at any given time.
  2. Now suppose that we also want the optimal sequence of edits. As we saw earlier, this problem can be recast in terms of a corresponding grid-shaped dag, in which the goal is to find the optimal path from node (0,0) to node (n,m). It will be convenient to work with this formulation, and while we’re talking about convenience, we might as well also assume that is a power of 2.
    Let’s start with a small addition to the edit distance algorithm that will turn out to be very useful. The optimal path in the dag must pass through an intermediate node (k,m2) for some k; show how the algorithm can be modified to also return this value k.
  3. Now consider a recursive scheme:
    Procedure find-path((0,0)(n,m))
    Compute the value kabove
    find-path ((0,0)k,m2)
    find-path k,m2n,m
    concatenate these two paths, with kin the middle.
    Show that this scheme can be made to run inO (mn) time and O(n) space.

Yuckdonald’s is considering opening a series of restaurant along Quaint Valley Highway(QVH). The n possible locations are along a straight line, and the distances of these locations from the start of QVH are, in miles and in increasing order,m1,m22,....,mn.. The constraints are as follows:

At each location, Yuckdonald may open at most one restaurant. The expected profit from opening a restaurant at location i is given aspi, wherepi>0andi=1,2,,n.

Any two restaurants should be at least k miles apart, where k is a positive integer.

Give an efficient algorithm to compute the maximum expected total profit subject to the given constraints.

A mission-critical production system has n stages that have to be performed sequentially; stage i is performed by machine Mi. Each machine Mi has a probability riof functioning reliably and a probability 1-riof failing (and the failures are independent). Therefore, if we implement each stage with a single machine, the probability that the whole system works is r1·r2···rn. To improve this probability we add redundancy, by having mi copies of the machine Mi that performs stage i. The probability that all mi copies fail simultaneously is only (1-ri)mi,so the probability that stage i is completed correctly is 1 − (1-ri)mi, and the probability that the whole system works isΠni=1(1-1-rimi).Each machine has a cost ci, and there is a total budget to buy machines. (Assume that B and ciare positive integers.) Given the probabilities r1·r2···rn, the costsc1,...,cn, and the budget find the redundanciesm1,...,mn that are within the available budget and that maximize the probability that the system works correctly.

Reconstructing evolutionary trees by maximum parsimony. Suppose we manage to sequence a particular gene across a whole bunch of different species. For concreteness, say there are n species, and the sequences are strings of length k over alphabet={A,C,G,T}. How can we use this information to reconstruct the evolutionary history of these species?

Evolutionary history is commonly represented by a tree whose leaves are the different species, whose root is their common ancestor, and whose internal branches represent speciation events (that is, moments when a new species broke off from an existing one). Thus we need to find the following:

• An evolutionary tree with the given species at the leaves.

• For each internal node, a string of length K: the gene sequence for that particular ancestor.

For each possible tree T annotated with sequencess(u)kat each of its nodes , we can assign a score based on the principle of parsimony: fewer mutations are more likely.

localid="1659249441524" score(T)=(u.v)E(T)(numberofpositionsonwhichs(u)ands(v)disagree)

Finding the highest-score tree is a difficult problem. Here we will consider just a small part of it: suppose we know the structure of the tree, and we want to fill in the sequences s(u) of the internal nodes u. Here’s an example with k=4 and n=5:


(a) In this particular example, there are several maximum parsimony reconstructions of the internal node sequences. Find one of them.

(b) Give an efficient (in terms of n and k ) algorithm for this task. (Hint: Even though the sequences might be long, you can do just one position at a time.)

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