ΡΥTHΟΝ Determine a function T(n) that relates input size n to number of runtime steps and the Big-O Time complexity for this T(n): Example only: T(n) = an^2 +k Соmplexity %3D O(m) def f4(n, list_of_length_n): print (list_of_length_n) T(n) = Complexity =
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- Suppose that your implementation of a particular algorithm appears in C++ as for (int pass = 1; pass <= n; pass++) { for (int index = 0; index < n; index++) { for (int count = 1; count < 10; count++) { ... } // end for } // end for } // end for The previous code shows only the repetition in the algorithm, not the computations that occur within the loops. These computations, however, are independent of n. What is the Big O of the algorithm?6.Coding-----""Euler's totient function, also known as phi-function ϕ(n),counts the number of integers between 1 and n inclusive,which are coprime to n.(Two numbers are coprime if their greatest common divisor (GCD) equals 1)."""def euler_totient(n): """Euler's totient function or Phi function. Time Complexity: O(sqrt(n)).""" result = n for i in range(2, int(n ** 0.5) + 1): if n % i == 0: while n % i == 0: n //= i.#4. Euler's totient function, also known as phi-function ϕ(n),counts the number of integers between 1 and n inclusive,which are coprime to n.(Two numbers are coprime if their greatest common divisor (GCD) equals 1)."""def euler_totient(n): """Euler's totient function or Phi function. Time Complexity: O(sqrt(n)).""" result = n for i in range(2, int(n ** 0.5) + 1): if n % i == 0: while n % i == 0: n //= i.
- A sequential search of a sorted list can halt when the target is less than a givenelement in the list. Define a modified version of this algorithm and state thecomputational complexity, using big-O notation, of its best-, worst-, and average-case performances. Using this template in python: def sequential_search(input_list, target):target_index = -1#TODO: Your work here# Return target_index. If not found, -1return target_indexif __name__ == "__main__":my_list = [1, 2, 3, 4, 5]print(sequential_search(my_list, 3)) # Correct Output: 2print(sequential_search(my_list, 0)) # Correct Output: -1Given below is the implementation of the bellman ford and dijkstras algorithm. Please complete the code for the time_shortest_path_algs() function according to the instructions in the 1st screenshot provided. Done in python 3.10 or later please def bellman_ford(self,s) : """Bellman Ford Algorithm for single source shortest path. Keyword Arguments: s - The source vertex. """ distances = {v: float('inf') for v in self.adjacency_list} distances[s] = 0 parents = {v: None for v in self.adjacency_list} for _ in range(len(self.adjacency_list) - 1): for from_vertex in self.adjacency_list: for to_vertex in self.adjacency_list[from_vertex]: if distances[from_vertex] + self.weights[(from_vertex, to_vertex)] < distances[to_vertex]: distances[to_vertex] = distances[from_vertex] + self.weights[(from_vertex, to_vertex)] parents[to_vertex] =…Computer Science Write the PSEUDOCODE for an algorithm that takes as input a list of numbers that are sorted in nondecreasing order, and finds the location(s) of the most frequently occurring element(s) in the list. If there are more than one element that is the most frequently occurring, then return the locations of all of them. Analyze the worst-case time complexity of this algorithm and give the O() estimate. (A list is in nondecreasing order if each number in the list is greater than or equal to the number preceding it.)
- Given an unsorted array A of integers of any size, n ≥ 3, and an integer value x, write an algorithm as a pseudo code (not a program!) that would find out if there exist EXACTLY3 occurrences in the array with value x. What is the time complexity of your algorithm, in terms of Big-O? What is the space complexity of your algorithm, in terms of Big-O? What if , the given array A is sorted. Will time complexity change from the case that A was unsorted? • If yes; give a new algorithm that achieves this better complexity (indicate the time complexity as of that algorithm). • If no, explain why such new constraints/conditions cannot lead to a better time complexity.Using Java, give an algorithm for the following problem: given a list of n distinct positive integers, partition the list into two sublists, each of size n/2, such that the difference between the sums of the integers in the sublists is minimized. Determine the time complexityof the algorithm.you may assume that h is a multiple of 2a) Consider a recursive function to return the Number of Binary Digits in the Binary Representation of a Positive Decimal Integer (n) using a recursive algorithm. int Process (int n) { if (n == 1) return 1; else return (Extra() + Process (n/4) + Process (n/4)); } Given that Extra(n) is a function of O(n)1) Find T(n) = number of arithmetic operations. 2) Calculate the complexity of this algorithm using Back Substitution.
- Please written by computer source Let x be a binary string. The minimal description of x, written d(x), is the shortest string ⟨M, w⟩ where TM M on input w halts with x on its tape. If several such strings exist, select the lexicographically first among them. The descriptive complexity, or Kolmogorov complexity, of x, written K(x), is K(x) = |d(x)|. Show that the function K(x) is not a computable function. HINTS: If K is a computable function, there is some TM which computes it. That TM can be used to find strings of large complexity. Try to design a program which outputs “complex” strings but which contradicts their supposed complexity, and even contradicting the supposed complexity of a single string suffices.Assume that each of the expressions below gives the processing time T(n) spent by an algorithm for solving a problem of size n. Select the dominant term(s) having the steepest increase in n and specify the lowest Big-Oh complexity of each algorithm. For example, the dominant term in 0.1n + 10n4 is 10n4 and it is O(n4). Expression Dominant term(s) O(. . .) 5 + 0.001n3 + 0.025n 500n + 100n1.5 + 50n log10 n 0.3n + 5n1.5 + 2.5 · n1.75 n2 log2 n + n(log2 n)2 n log3 n + n log2 n 100n + 0.01n2 0.01n + 100n2 2n + n0.5 + 0.5n1.25 0.01n log2 n + n(log2 n)2 100n log3 n + n3 + 100nBig-O notation. Suppose n is the input size, we have the following commonly seen functions in complexity analysis: f1(n) = 1, f2(n) = log n, f3(n) = n, f4(n) = n log n, f5(n) = n2, f6(n) = 2n, f7(n) = n!, f8(n) = nn. Intuitively, the growth rate of the functions satisfy 1 < log n < n < n log n < n2 < 2n < n! < nn. Prove this is true. Let f, g : N → R+, prove that Ω(f(n) + g(n)) = Ω(max{f(n), g(n)}). [Note: Proving this will help you understand that we can also leave out the insignificant parts in big-Ω notation and the result is still a lower bound, e.g., Ω(n2 + n log n + n) = Ω(n2).]