Solve all of them! 9. Divide-and-Conquer and Dynamic Programming. This question has been on a final exam.(a) What does dynamic programming have in common with divide-and-conquer? What is a principaldifference between them?(b) A recurrence for the number of combinations of m things out of n, ("), for n > 1 and 0< m < n:nif m = 0 or m = m(") + () if 0 < m < nHere is a recursive function comb taking two integer parameters n and m that returns (").int comb( int n, int m ){0 ) || ( m ==if( ( m ==n ) )return 1;else return( C( n-1,m ) + C( n-1, m-1 ) );}Though this is not an optimization problem, convince yourself that the function computes manysubproblems over and over and that a dynamic programming approach can be applied to thisproblem. Consider drawing the recursion tree to see this.i. Modify the function comb to compute () using a top-down dynamic programming approachusing memoization (i.e., , a table). The table required here is two-dimensional, n x m.ii. Solve the problem bottom-up with memoization.

Answer a) Both Dynamic programming and Divide-and-conquer solve a bigger problem using solutions to…

(a) What does dynamic programming have in common with divide-and-conquer? What is a principal difference between them? (b) A recurrence for the number of combinations of m things out of n, ("), for n > 1 and 0

(a) What does dynamic programming have in common with divide-and-conquer? What is a principal difference between them? (b) A recurrence for the number of combinations of m things out of n, ("), for n > 1 and 0

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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