Give the tightest bound in terms of Big O public type something(n){ result =...
Give the tightest bound in terms of Big O
public type something(n){
result = 0;
while (n > 1){
n /= 2;
result += 1;
}
return result;
}
Homework Answers
Loop runs for values of n from n,n/2,n/4,...,16,8,4,2,1 So, number of times loop runs = log2(n) Time complexity = O(number of times loop runs) = O(log2(n))
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Give the tightest bound in terms of Big O
public type something(n){
result =...
Give the tightest bound in terms of Big O public type foo(n, a[]){ for (i=0, i<n;...
Give the tightest bound in terms of Big O public type foo(n, a[]){ for (i=0, i<n; i++){ if (a[i] == 0) return 0; } return 1; }
what is the tightest big-o bound on the size of the stack during depth first search...
what is the tightest big-o bound on the size of the stack during depth first search of a tree starting at the root, where n is the number of nodes in the tree? and why?
PYTHON: Im stuck here, big O notation and runtime. What is it and Why are they...
PYTHON: Im stuck here, big O notation and runtime. What
is it and Why are they those? Please look at the pic, need help as
Im confused. Thank You!
def method3(n): for i in range(n): for j in range(100): for k in range(n): print(i+j+k) What is the runtime (tightest/closest bound in terms of O) for the above python function (method 3)? Please briefly explain. Enter your answer here def method4(n): for i in range(n): for j in range(n, o, -2):...
(10') 6. For each of the following code blocks, write the best (tightest) big-o time complexity...
(10') 6. For each of the following code blocks, write the best (tightest) big-o time complexity i) for (int i = 0; ǐ < n/2; i++) for (int j -0: ni j++) count++ i) for (int í = 0; i < n; i++) for (int ni j0 - for (int k j k ni kt+) count++ İİİ) for (int í ー 0; i < n; i++) for(int j = n; j > 0; j--) for (int k = 0; k...
For each algorithm, give a reasonable big-O bound on its worst-case running time. Omit unnecessary terms...
For each algorithm, give a reasonable big-O bound on its worst-case running time. Omit unnecessary terms and constants in your bound, for example, don't say O(2n22n 1), say O(n2). (In most cases, these aren't the best possible algorithms for each task!) Briefly explain your reasoning in each case.
1, Variation on 3.3#4] Give a big-O estimate in terms of n for the number of...
1, Variation on 3.3#4] Give a big-O estimate in terms of n for the number of oper- ations used in this segment of an algorithm, where an operation is an addition or a multiplication, (ignoring comparisons used to test the conditions in the while loop). while i 〈 n j:= j + i [10 points]
For each C++ function below, give the tightest can asymptotic upper bound that you can determine....
For each C++ function below, give the tightest can asymptotic upper bound that you can determine. (a) void mochalatte(int n) { for (int i = 0: i < n: i++) { count < < "iteration;" < < i < < end1: } } (b) void nanaimobar (int n) { for (int i = 1: i < 2*n: i = 2*i) { count < < "iteration;" < < i < < end1: } } void appletart (int n) { for (int...
1(5 pts): For each code fragment below, give the complexity of the algorithm (O or Θ)....
1(5 pts): For each code fragment below, give the complexity of the algorithm (O or Θ). Give the tightest possible upper bound as the input size variable increases. The input size variable in these questions is exclusively n. Complexity Code public static int recursiveFunction (int n)f f( n <= 0 ) return 0; return recursiveFunction (n - 1) 1; for(int i 0i <n; i+) j=0; for ( int j k=0; i; k < < j++) for (int j; m <...
Need to find number of elementary expressions in terms of n, not looking for Big O...
Need to find number of elementary expressions in terms
of n, not looking for Big O complexity.
4. Work out the number of elementary operations in the worst possible case and the best possible case for the following algorithm (justify your answer): 0: function Nonsense (positive integer n) 1: it1 2: k + 2 while i<n do for j+ 1 to n do if j%5 = 0 then menin else while k <n do constant number C of elementary operations...
Give a big-Oh characterization, in terms of n,of the running time for each of the following...
Give a big-Oh characterization, in terms of n,of the running time for each of the following code segments (use the drop-down): - public void func1(int n) { A. @(1). for (int i = n; i > 0; i--) { System.out.println(i); B. follogn). for (int j = 0; j <i; j++) System.out.println(j); c.e(n). System.out.println("Goodbye!"); D.@(nlogn). E.e(n). F.ein). public void func2 (int n) { for (int m=1; m <= n; m++) { system.out.println (m); i = n; while (i >0){ system.out.println(i); i...
PYTHON: Im stuck here, big O notation and runtime. What
is it and Why are they those? Please look at the pic, need help as
Im confused. Thank You!
def method3(n): for i in range(n): for j in range(100): for k in range(n): print(i+j+k) What is the runtime (tightest/closest bound in terms of O) for the above python function (method 3)? Please briefly explain. Enter your answer here def method4(n): for i in range(n): for j in range(n, o, -2):...
(10') 6. For each of the following code blocks, write the best (tightest) big-o time complexity i) for (int i = 0; ǐ < n/2; i++) for (int j -0: ni j++) count++ i) for (int í = 0; i < n; i++) for (int ni j0 - for (int k j k ni kt+) count++ İİİ) for (int í ー 0; i < n; i++) for(int j = n; j > 0; j--) for (int k = 0; k...
For each algorithm, give a reasonable big-O bound on its worst-case running time. Omit unnecessary terms and constants in your bound, for example, don't say O(2n22n 1), say O(n2). (In most cases, these aren't the best possible algorithms for each task!) Briefly explain your reasoning in each case.
1, Variation on 3.3#4] Give a big-O estimate in terms of n for the number of oper- ations used in this segment of an algorithm, where an operation is an addition or a multiplication, (ignoring comparisons used to test the conditions in the while loop). while i 〈 n j:= j + i [10 points]
For each C++ function below, give the tightest can asymptotic upper bound that you can determine. (a) void mochalatte(int n) { for (int i = 0: i < n: i++) { count < < "iteration;" < < i < < end1: } } (b) void nanaimobar (int n) { for (int i = 1: i < 2*n: i = 2*i) { count < < "iteration;" < < i < < end1: } } void appletart (int n) { for (int...
1(5 pts): For each code fragment below, give the complexity of the algorithm (O or Θ). Give the tightest possible upper bound as the input size variable increases. The input size variable in these questions is exclusively n. Complexity Code public static int recursiveFunction (int n)f f( n <= 0 ) return 0; return recursiveFunction (n - 1) 1; for(int i 0i <n; i+) j=0; for ( int j k=0; i; k < < j++) for (int j; m <...
Need to find number of elementary expressions in terms
of n, not looking for Big O complexity.
4. Work out the number of elementary operations in the worst possible case and the best possible case for the following algorithm (justify your answer): 0: function Nonsense (positive integer n) 1: it1 2: k + 2 while i<n do for j+ 1 to n do if j%5 = 0 then menin else while k <n do constant number C of elementary operations...
Give a big-Oh characterization, in terms of n,of the running time for each of the following code segments (use the drop-down): - public void func1(int n) { A. @(1). for (int i = n; i > 0; i--) { System.out.println(i); B. follogn). for (int j = 0; j <i; j++) System.out.println(j); c.e(n). System.out.println("Goodbye!"); D.@(nlogn). E.e(n). F.ein). public void func2 (int n) { for (int m=1; m <= n; m++) { system.out.println (m); i = n; while (i >0){ system.out.println(i); i...
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