Calculate the number of steps and find the big-O notation of the program segment below: Algorithm i. Algorithm ii. iii. Discuss the complexity of Algorithm i and ii. for (int a=3; a<=n; a++) for (int a=1; a
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Calculate the number of steps and find the big-O notation of the program segment below:
-
Algorithm i. -
Algorithm ii.
iii. Discuss the complexity of Algorithm i and ii.
cout<<a;
}
cout<<a;
}
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- The two different algorithms that perform the same complexity process are called B and C. The total number of operations (operations) performed with algorithm B is 64n. The total number of operations performed with the C algorithm is 16 n ^ 2. Here is my question: Determine the problem size n0 where B is better than C for n> n0 please.Write a short introduction on algorithm complexity, followed by explaining why it is important, useful and/or exciting. Thanks!please answer with proper explanation and step by step. don't give Direct answer. Question: If an algorithm takes n^2 operations, and the computer can perform 100 billion operations per second, what is a reasonable estimate of how long it will take to sort 2 billion elements (assuming there is ample memory, the computer doesn't crash, etc.)? a. 1 second b. 1 minute c. 1 hour d. 1 day e. 1 month f. 1 year g. 1 decade h. 1 century
- What does computable mean? (May have more than one correct answer) A.) An algorithm is considered computable if it can be encoded as a program on a computer. B.) A problem is computable if an algorithm exists for solving it. C.) An algorithm is considered computable if it is the most efficient algorithm. D.) A problem is computable if a computer program exists for solving it.What is the difference between an algorithm and a heuristic, and how do these approaches differ in their effectiveness for solving complex computational problems?Solve the Subparts A&B thank u and please no reject NOTE LO1: Explain fundamental concept of analysis arithms. LO2: Apply algorithm techniques and methods. LO3: Solve a problem using specific algorithm. LO4: Compare several algorithm design methods.
- Suppose an algorithm requires 2n steps to complete a task for an input of size n. If the size of the input is doubled, what happens to the number of steps? The number of steps for the larger input is equal to the number of steps for the original input. The number of steps for the larger input is twice the number of steps for the original input. The number of steps for the larger input is four times the number of steps for the original input. The number of steps for the larger input is the square of the number of steps for the original input. The number of steps for the larger input is more than the number of steps for the original input, but is none of the above.Read each sentence and identify if it is True or False. 1. Can an instruction or rule in an algorithm have more than one meaning? 2. Can an algorithm be specified in an infinite number of steps?q10) Analysis of an Algorithm with respective CPU time taken by algorithm to execute is called ______________. a. memory complexity b. time complexity c. space complexity d. execution complexity.
- Could you please explain and compare time complexity for each algorithm ClustalW, ClustalOmega, MAFFT, MUSCLE, TCOFFE?Q1.a Time complexity gives a rough idea of how long it will take for an algorithm to execute based on two things: the size of the input it has and the amount of steps it takes to complete, whereas space complexity is also the amount of memory required to execute an algorithm based on the size of input it has or given.Using mathematical notations or diagrams critically explain the time and space complexity of the code below. int a = 0;for (i = 0; i < N; i++) {for (j = N; j > i; j--) {a = a + i + j; }}b. i. What does it mean when we say that an algorithm A is asymptotically more efficient than algorithm B?ii. Algorithm A and B have a worst-case running time of O(n) and O(logn), respectively. Identify the algorithm that runs fast and critically explain why.Q1.a Time complexity gives a rough idea of how long it will take for an algorithm to execute based on two things: the size of the input it has and the amount of steps it takes to complete,whereas space complexity is also the amount of memory required to execute an algorithm based on the size of input it has or given.Using mathematical notations or diagrams critically explain the time and space complexity of the code below. int a = 0;for (i = 0; i < N; i++) {for (j = N; j > i; j--) {a = a + i + j;}}