Homework Answers
The time complexity for computing all permutations of a set is O(N!)-(option 1)
T(n)=n+n*T(n-1)
by solving the above equation, it results in
T(n)=(k+2)*n! where k is constant, by applying the rules of Big O, constant is ignored and the resultant time complexity is O(n!).
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What is the time complexity for computing all permutations of a set? O O(N!) OO(N3) OO(N)...
Suppose that Algorithm A has runtime complexity O(n3) and Algorithm B has runtime complexity O(n logn),...
Suppose that Algorithm A has runtime complexity O(n3) and Algorithm B has runtime complexity O(n logn), where both algorithms solve the same problem. (a) How do the algorithms compare when n = 12? (b) How do the algorithms compare when n is very large?
What is the time complexity (Big-O) of the following code? class Main { // Recursive...
What is the time complexity (Big-O) of the following code? class Main { // Recursive function to generate all permutations of a String private static void permutations(String candidate, String remaining) { if (remaining.length() == 0) { System.out.println(candidate); } for (int i = 0; i < remaining.length(); i++) { String newCandidate = candidate + remaining.charAt(i); String newRemaining = remaining.substring(0, i) +...
Match each problem to the time complexity class it likely belongs to: 1. O(1): Constant 2....
Match each problem to the time complexity class it likely belongs to: 1. O(1): Constant 2. O(n): Linear 3. O(n!): Factorial 4. O(logn): Logarithmic 5.O(n2): Quadratic 6. O(n3): Cubic OPTIONS: a. Find an element in an unsorted array b. Shortest path between two nodes in a graph c. Matrix multiplication d. Generate permutations of a string e. Add an element to the front of a linked list f. Find an element in a binary search tree
. Consider the problem of generating all the possible permutations of length n. For example, the...
. Consider the problem of generating all the possible permutations of length n. For example, the permutations of length 3 are: {1,2,3}, {2,1,3},{2,3,1}, {1,3,2}, {3,1,2}, {3,2,1}. Write a Well documented pseudocode of a non-recursive algorithm that computes all the permutations of size n. The only data structure allowed is a queue. Any other memory usage should be O(1). Calculate the time complexity of your algorithm using the big-Oh notation. Show all calculations. (The code should be written in Java!!)
Please compute the time complexity of the algorithm. (ex: O(N) or O(N log N)) void Set::unite(const...
Please compute the time complexity of the algorithm. (ex: O(N)
or O(N log N))
void Set::unite(const Set& seti, const Set& set2) { const Set* sp = &set2; if (this == &seti) { if (this == &set2) return; } else if (this == &set2) sp = &setl; else { *this = seti; if (&setl == &set2) return; } Node* pl = m_head->m_next; Node* p 2 = sp->m_head->m_next; while (pl != m_head & & p2 != sp->m_head) { if (pl->m_value < p2->m_value)...
What is the time complexity of this code? I'm unsure if it is O(log(n)) or O(n)....
What is the time complexity of this code?
I'm unsure if it is O(log(n)) or O(n).
I think that the while loop is logn but the for loop that comes
after runs the same number of times as the while loop.
string toBinary(int num) { string binary = "", temp = ""; while (num != 0) { temp += to_string(num%2); num /= 2; for (int i = temp.size() - 1; i >= 0; i--) { binary += temp[i]; return binary;
Using C++ please explain What is the Big-O time complexity of the following code: for (int...
Using C++ please explain
What is the Big-O time complexity of the following code: for (int i=0; i<N; i+=2) { ... constant time operations... Select one: o a. O(n^2) O b. O(log n) c. O(n) O d. 0(1) What is the Big-O time complexity of the following code: for(int i=1; i<N; i*=2) { ... constant time operations... Select one: O O a. O(n^2) b. 0(1) c. O(n) d. O(log n) O What is the Big-O time complexity of the following...
a) Prove that running time T(n)=n3+30n+1 is O(n3) [1 mark] b) Prove that running time T(n)=(n+30)(n+5)...
a) Prove that running time T(n)=n3+30n+1 is O(n3) [1 mark] b) Prove that running time T(n)=(n+30)(n+5) is O(n2) [1 mark] c) Count the number of primitive operation of algorithm unique1 on page 174 of textbook, give a big-Oh of this algorithm and prove it. [2 mark] d) Order the following function by asymptotic growth rate [2 mark] a. 4nlogn+2n b. 210 c. 3n+100logn d. n2+10n e. n3 f. nlogn
Consider the following recursive algorithm for computing the sum of the first n cubes: S(n) =...
Consider the following recursive algorithm for computing the sum
of the first n cubes: S(n) = 13 +
23 + … + n3.
(a) Set up a recurrence relation for the number of
multiplications made by this algorithm.
(b) Provide an initial condition for the
recurrence relation you develop at the question (a).
(c) Solve the recurrence relation of the
question (a) and present the time complexity as described at the
question number 1.
Algorithm S n) Input: A positive...
What is the time complexity of the following code segment? for (int i = 0; i<n;...
What is the time complexity of the following code segment? for (int i = 0; i<n; i--) if (a[i] != 0) sum = a[i]; What is the time complexity of the following code segment? for (int i = 0; i<10; i++) if (a[i] != 0) sum += a[i]; What is the time complexity of the following code segment? for (int i = 0; i<n/2; i++) if (a[i] != 0) sum += a[i]; What is the time complexity of the following...
Suppose that Algorithm A has runtime complexity O(n3) and Algorithm B has runtime complexity O(n logn), where both algorithms solve the same problem. (a) How do the algorithms compare when n = 12? (b) How do the algorithms compare when n is very large?
Please compute the time complexity of the algorithm. (ex: O(N)
or O(N log N))
void Set::unite(const Set& seti, const Set& set2) { const Set* sp = &set2; if (this == &seti) { if (this == &set2) return; } else if (this == &set2) sp = &setl; else { *this = seti; if (&setl == &set2) return; } Node* pl = m_head->m_next; Node* p 2 = sp->m_head->m_next; while (pl != m_head & & p2 != sp->m_head) { if (pl->m_value < p2->m_value)...
What is the time complexity of this code?
I'm unsure if it is O(log(n)) or O(n).
I think that the while loop is logn but the for loop that comes
after runs the same number of times as the while loop.
string toBinary(int num) { string binary = "", temp = ""; while (num != 0) { temp += to_string(num%2); num /= 2; for (int i = temp.size() - 1; i >= 0; i--) { binary += temp[i]; return binary;
Using C++ please explain
What is the Big-O time complexity of the following code: for (int i=0; i<N; i+=2) { ... constant time operations... Select one: o a. O(n^2) O b. O(log n) c. O(n) O d. 0(1) What is the Big-O time complexity of the following code: for(int i=1; i<N; i*=2) { ... constant time operations... Select one: O O a. O(n^2) b. 0(1) c. O(n) d. O(log n) O What is the Big-O time complexity of the following...
Consider the following recursive algorithm for computing the sum
of the first n cubes: S(n) = 13 +
23 + … + n3.
(a) Set up a recurrence relation for the number of
multiplications made by this algorithm.
(b) Provide an initial condition for the
recurrence relation you develop at the question (a).
(c) Solve the recurrence relation of the
question (a) and present the time complexity as described at the
question number 1.
Algorithm S n) Input: A positive...
What is the time complexity of the following code segment? for (int i = 0; i<n; i--) if (a[i] != 0) sum = a[i]; What is the time complexity of the following code segment? for (int i = 0; i<10; i++) if (a[i] != 0) sum += a[i]; What is the time complexity of the following code segment? for (int i = 0; i<n/2; i++) if (a[i] != 0) sum += a[i]; What is the time complexity of the following...
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