Suppose two teams, A and B, are playing a match to see who is the first to win games (for some particular n). We can suppose that A and B are equally competent, so each has a 50% chance of winning any particular game. Suppose they have already played i+j games, of which A has won i and B has won j. Give an efficient algorithm to compute the probability that A will go on to win the match. For example, if i=n-1 and j=n-3 then the probability that A will win the match is 78, since it must win any of the next three games.

Short Answer

Expert verified

To solve the given problem, use dynamic programming approach.78,i=n-1andj=n-3,i+j

Step by step solution

01

Dynamic programming approach.

In dynamic programming there are all possibilities and more time as compared to greedy programming. and the Dynamic programming approach always gives the accurate or correct answer. In dynamic programming have to compute only distinct function call because as soon as compute and store in one data structure so that after this reuse afterward if it is needed.

02

Defining Recursive Relation and Implementation of Algorithm.

Let us suppose P (i,j ) is the probability for A to win the first n games.

Given that, i = Number of match A has won

And j=Number of match B has won

So, there are two conditions they are,

If P (i,j )=1 where i > 0 and j=1 this mean the probability of A to win is ‘one’.

If P (i,j )=0 where i > 0 and j=1 , this means the probability of A for won is ‘zero’.

Now for rest of the value of 'i' and 'j' then the value of evaluations is,

pi,j=12Pi,j+1+Pi+1

After that let P (0,j ) is as zero or one then compare each situation.

P0,i=1andPi,1=0ifn-j>0andn-i=0Pn-i,j-i=1ifn-j=0andn-i>1

Here, A will go on to win the match. For example, if i=n-1 and j=n-3 then the probability that A will win the match is 78, since it must win any of the next three games.

ifn-i>1andn-j>1Pn-i,j-i=12Pi-1,j+Pi,j-2returnPn-i,j-i

By approach of dynamic programming for playing a match to see who is the first to win number of games where equally competent and each has fifty percent chance of winning any particular game and the probability that A will go for to win the match an A will go on to win the match. ifi=n-1 and j=n-3 then the probability that A will win and it will take time of O(n) Since, we are playing ‘n’ times, so our algorithm will trace output of n output. Thus, runtime would be O(n)

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Most popular questions from this chapter

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.

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.

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.).

You are given a string of n characters s[1...n], which you believe to be a corrupted text document in which all punctuation has vanished (so that it looks something like “itwasthebestoftimes...”). You wish to reconstruct the document using a dictionary, which is available in the form of a Boolean function dict(.): for any string w,

dict(w)={trueifwisavalidwordfalseotherwise

Give a dynamic programming algorithm that determines whether the string s[.]can be reconstituted as a sequence of valid words. The running time should be at mostO(n2) , assuming calls to dict take unit time.

In the event that the string is valid, make your algorithm output the corresponding sequence of words.

Local sequence alignment. Often two DNA sequences are significantly different, but contain regions that are very similar and are highly conserved. Design an algorithm that takes an input two strings x[1Kn]and y[1Km]and a scoring matrix δ(as defined in Exercise 6.26), and outputs substrings x'andy'of x and y respectively, that have the highest-scoring alignment over all pairs of such substrings. Your algorithm should take time O(mn).

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