the for loop is basically a repitation or traversing loop means one's to write his name 100 times he use the for loop
example
for(i=0;i<=100;i++)
{
printf("my name is AA");
}
the for loop checks the condition until i<=100 repeates the name or check the condition upto 100 times and then terminates.
A for loop is a loop of the form:
for (/*starting conditions/variable initialisation*/;/*condition*/;/*changes to variables*/) {}
The most basic example would be:
for (int i=0;i<10;i++) {
prinf("%i",i);
}
This code can be represented as the following algorithm:
The standard algorithm, for any 4 statements:
for (/*EXPRESSION 1*/;/*EXPRESSION 2*/;/*EXPRESSION 3*/) {
/*STATEMENT 4*/
}
would be:
No. An algorithm is a procedure or formula for solving a problem: a finite series of computation steps to produce a result. Some algorithms require one or more loops, but it is not true that every algorithm requires a loop.
n-1 times
n=100 loop until n = 9 print n n = n -1 end loop
The three primitive logic structures in programming are selection, loop and sequence. Any algorithm can be written using just these three structures.
The LCM can be calculated without using any loop or condition as follows: int lcm (int a, int b) { return a / gcd (a, b) * b; } The problem is that the typical implementation for the GCD function uses Euclid's algorithm, which requires a conditional loop: int gcd (int a, int b) { while (b!=0) b ^= a ^= b ^= a %= b; return a; } So the question is really how do we calculate the GCD without a conditional loop, not the LCM. The answer is that we cannot. There are certainly alternatives to Euclid's algorithm, but they all involve conditional loops. Although recursion isn't technically a loop, it still requires a conditional expression to terminate the recursion.
The time complexity of a while loop in an algorithm is typically represented as O(n), where n is the number of iterations the loop performs.
No. An algorithm is a procedure or formula for solving a problem: a finite series of computation steps to produce a result. Some algorithms require one or more loops, but it is not true that every algorithm requires a loop.
Using loop invariant.
Not used
The big O notation is important in analyzing the efficiency of algorithms. It helps us understand how the runtime of an algorithm grows as the input size increases. In the context of the outer loop of a program, the big O notation tells us how the algorithm's performance is affected by the number of times the loop runs. This helps in determining the overall efficiency of the algorithm and comparing it with other algorithms.
Algorithm: 1. From the user collect the integer whose table is required 2. Use a loop with loop counter starting with 0x01 and ends till the table value is required 3. start multiplication the input number with the loop variable and print the result.
The usual definition of an algorithm's time complexity is called Big O Notation. If an algorithm has a value of O(1), it is a fixed time algorithm, the best possible type of algorithm for speed. As you approach O(∞) (a.k.a. infinite loop), the algorithm takes progressively longer to complete (an algorithm of O(∞) would never complete).
An example of finiteness in algorithm is when a loop within the algorithm has a predetermined number of iterations, meaning it will only run a specific number of times before completing. This ensures that the algorithm will eventually terminate and not run indefinitely.
Dijkstra's algorithm does not work with negative weights because it assumes that all edge weights are non-negative. Negative weights can cause the algorithm to give incorrect results or get stuck in an infinite loop.
n-1 times
n=100 loop until n = 9 print n n = n -1 end loop
#define int main (void){/*Algorithm*/1. First Initialize the number that is Random no2. Using while loop if the person got it, then the loop stop if not the loop continues.}return(0);