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Show that the number of binary search trees with \(n\) keys is given by the formula \\[\frac{1}{(n+1)}\left(\begin{array}{c}2 n \\\n\end{array}\right)\\]

Short Answer

Expert verified
The number of binary search trees with \(n\) keys follows the \(n^{th}\) Catalan Number, which is calculated by the formula \[\frac{1}{n+1}\binom{2n}{n} \]. This was proven by establishing a link between the recursive formula for the count of binary search trees and the recursive expression for Catalan Numbers.

Step by step solution

01

Understand the Problem

The problem is to prove the formula for the number of binary search trees possible with \(n\) keys. A binary search tree is a tree data structure in which each node has at most two children, referred to as the left child and the right child.
02

Find the Recursive Formula

For a BST, the number of total trees possible is the sum of the product of the number of trees possible in the left sub-tree and the right sub-tree. This can be represented with the recursive formula as: \[T(n) = \Sigma_{i=0}^{n-1} T(i) * T(n-i-1)\]
03

Link Recursive Form with Catalan Numbers

There is a pattern of numbers known as the Catalan Numbers. The \(n^{th}\) Catalan number is given by the formula: \[C(n) = \frac{1}{n+1} \binom{2n}{n}\] and the recursive formula for Catalan numbers is exactly the same as the formula derived in Step 2. This means that BST count is the same as Catalan numbers.
04

Conclusion

Then, from Step 2 and Step 3 it follows that the formula \[\frac{1}{(n+1)}\binom{2n}{n}\] is indeed the formula for calculating the number of distinct binary search trees with \(n\) keys.

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Most popular questions from this chapter

Let us consider two sequences of characters \(S_{1}\) and \(S_{2}\), For example, we could have \(S_{1}=\) ASCMA \(^{*}\) MN and \(S_{2}=\) AXMC4ANR. Assuming that a subsequence of a sequence can be constructed by deleting any number of characters from any positions, use the dynamic programming approach to create an algorithm that finds the longest common subsequence of \(S_{1}\) and \(S_{2}\) This algorithm returns the maximum-length common subsequence of each sequence.

Find an optimal circuit for the weighted, direct graph represented by the following matrix \(W\). Show the actions step by step. \\[W=\left[\begin{array}{rrrrr}0 & 8 & 13 & 18 & 20 \\\3 & 0 & 7 & 8 & 10 \\\4 & 11 & 0 & 10 & 7 \\ 6 & 6 & 7 & 0 & 11 \\\10 & 6 & 2 & 1 & 0\end{array}\right]\\]

Generalize the Optimal Binary Search Tree algorithm (Algorithm 3.9 ) to the case where the search key may not be in the tree. That is, you should let \(q_{i}\) where \(i=0,1,2, \ldots, n,\) be the probability that a missing search key can be situated between \(K e y_{i}\) and \(K e y_{i+1}\). Analyze your generalized algorithm, and show the results using order notation.

Write an efficient algorithm that will find an optimal order for multiplying \(n\) matrices \(A_{1} \times A_{2} \times \ldots \times A_{2}\) where the dimension of each matrix is \(1 \times 1\) \(1 \times d, d \times 1,\) or \(d \times d\) for some positive integer \(d .\) Analyze your algorithm, and show the results using order notation.

Can Floyd's Algorithm for the Shortest Paths Problem 2 (Algorithm 3.4 ) be used to find the shortest paths in a graph with some negative weights? Justify your answer.

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