7. What is the worst-case running time complexity of an algorithm with the recurrence relation T(N)...
7. What is the worst-case running time complexity of an algorithm with the recurrence relation T(N) = 2T(N/4) + O(N2)? Hint: Use the Master Theorem.
Homework Answers
Solve this recurrence relation using Master theorem.
Worse Time complexity = O(N2)
Add Answer to:
7. What is the worst-case running time complexity of an
algorithm with the recurrence relation T(N)...
FOR ALGORITHM A WORST CASE TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n)= n/ T (n...
FOR ALGORITHM A WORST CASE TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n)= n/ T (n )thi T (c)=1 if c < 100 FOR ALGORITHM B WORST TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n) = 2T (2/2) + n/logn ; (c) = 1 fc 2100 WHICH ALGORITHM IS ASYMPTOTICALLY FASTER? WHY?
Write a recurrence relation describing the worst case running time of each of the following algorithms,...
Write a recurrence relation describing the worst case running time of each of the following algorithms, and determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution by using substitution or a recursion tree. You may NOT use the Master Theorem. Simplify your answers, expressing them in a form such as O(nk) or (nklog n) whenever possible. If the algorithm takes exponential time, then just give an exponential lower bound using the 2 notation. function...
Write a recurrence relation describing the worst-case running time of each of the following algor...
Write a recurrence relation describing the worst-case
running time of each of the following algorithms and determine the
asymptotic complexity of the function defined by the recurrence
relation. Justify your solution by using substitution or a
recursion tree. You may NOT use the Master Theorem.
上午1:46 3月21日周四 令52%. " 5. endfor 6. return (r); function func4(A, n) *Aarray of n integers */ 1. if n s 20 then return (A[n]); 4. while (i < n/2) do 7. endwhile 8. x...
The worst-case complexity of the Quicksort is O(n2). (a) Use the selection algorithm to modify Quicksort...
The worst-case complexity of the Quicksort is O(n2). (a) Use the selection algorithm to modify Quicksort and reduce its worst-case complexity to O( n*log(n) ) (b) Write the recurrence equation for the modified Quicksort algorithm.
Consider the merge sort algorithm. (a) Write a recurrence relation for running time function for the...
Consider the merge sort algorithm. (a) Write a recurrence relation for running time function for the merge sort. (b) Use two methods to solve the recurrence relation. (c) What is the best, worst and average running time of the merge sort algorithm? Justify your answer.
3. Determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution...
3. Determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution using expansion/substitution and upper and/or lower bounds, when necessary. You may not use the Master Theorem as justification of your answer. Simplify and express your answer as O(n*) or O(nk log2 n) whenever possible. If the algorithm is exponential just give exponential lower bounds c) T(n) T(n-4) cn, T(0) c' d) T(n) 3T(n/3) c, T() c' e) T(n) T(n-1)T(n-4)clog2n, T(0) c'
3. Determine the...
Give an algorithm with the following properties. • Worst case running time of O(n 2 log(n))....
Give an algorithm with the following properties. • Worst case running time of O(n 2 log(n)). • Average running time of Θ(n). • Best case running time of Ω(1).
Explain the Karatsuba-Ofman algorithm to multiply 2 n-bit integers. Derive a recurrence relation for its complexity and solve this recurrence relation.
Explain the Karatsuba-Ofman algorithm to multiply 2 n-bit integers. Derive a recurrence relation for its complexity and solve this recurrence relation.
Analysis Divide & Conquer: Analyze the complexity of algorithm A1 where the problem of size n...
Analysis Divide & Conquer: Analyze the complexity of algorithm A1 where the problem of size n is solved by dividing into 4 subprograms of size n - 4 to be recursively solved and then combining the solutions of the subprograms takes O(n2) time. Determine the recurrence and whether it is “Subtract and Conquer” or “Divide and Conquer“ type of problem. Solve the problem to the big O notation. Use the master theorem to solve, state which theorem you are using...
Exercise 7.3.5: Worst-case time complexity - mystery algorithm. The algorithm below makes some changes to an...
Exercise 7.3.5: Worst-case time complexity - mystery algorithm. The algorithm below makes some changes to an input sequence of numbers. MysteryAlgorithm Input: a1, a2....,an n, the length of the sequence. p, a number Output: ?? i != 1 j:=n While (i < j) While (i <j and a < p) i:= i + 1 End-while While (i <j and a 2 p) j:=j-1 End-while If (i < j), swap a, and a End-while Return( aj, a2,...,an) (a) Describe in English...
FOR ALGORITHM A WORST CASE TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n)= n/ T (n )thi T (c)=1 if c < 100 FOR ALGORITHM B WORST TIME COMPLEXITY IS DESCRIBED BY RECURRENCE FORMULA T(n) = 2T (2/2) + n/logn ; (c) = 1 fc 2100 WHICH ALGORITHM IS ASYMPTOTICALLY FASTER? WHY?
Write a recurrence relation describing the worst case running time of each of the following algorithms, and determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution by using substitution or a recursion tree. You may NOT use the Master Theorem. Simplify your answers, expressing them in a form such as O(nk) or (nklog n) whenever possible. If the algorithm takes exponential time, then just give an exponential lower bound using the 2 notation. function...
Write a recurrence relation describing the worst-case
running time of each of the following algorithms and determine the
asymptotic complexity of the function defined by the recurrence
relation. Justify your solution by using substitution or a
recursion tree. You may NOT use the Master Theorem.
上午1:46 3月21日周四 令52%. " 5. endfor 6. return (r); function func4(A, n) *Aarray of n integers */ 1. if n s 20 then return (A[n]); 4. while (i < n/2) do 7. endwhile 8. x...
3. Determine the asymptotic complexity of the function defined by the recurrence relation. Justify your solution using expansion/substitution and upper and/or lower bounds, when necessary. You may not use the Master Theorem as justification of your answer. Simplify and express your answer as O(n*) or O(nk log2 n) whenever possible. If the algorithm is exponential just give exponential lower bounds c) T(n) T(n-4) cn, T(0) c' d) T(n) 3T(n/3) c, T() c' e) T(n) T(n-1)T(n-4)clog2n, T(0) c'
3. Determine the...
Exercise 7.3.5: Worst-case time complexity - mystery algorithm. The algorithm below makes some changes to an input sequence of numbers. MysteryAlgorithm Input: a1, a2....,an n, the length of the sequence. p, a number Output: ?? i != 1 j:=n While (i < j) While (i <j and a < p) i:= i + 1 End-while While (i <j and a 2 p) j:=j-1 End-while If (i < j), swap a, and a End-while Return( aj, a2,...,an) (a) Describe in English...
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