Answer:
A.
Explanation:
The rest is nearly impossible to detect or not worth the time.
If an algorithm's resource consumption, often referred to as computational cost, is at or below a certain threshold, it is said to be efficient. Generally speaking, "acceptable" indicates that it will operate on a machine that is available in a fair amount of time or space, usually based on the size of the input.
<h3>Explain about the efficiency of an algorithm?</h3>
Growth requires an understanding of an algorithm's effectiveness. Programmers write code with the future in mind, and efficiency is essential to achieve this. Reducing the number of iterations required to finish your task in relation to the size of the dataset is the goal of efficient algorithm development.
The use of asymptotic analysis can frequently help to solve these issues. As the size of the input increases, asymptotic analysis quantifies an algorithm's effectiveness or the program that implements it.
To express how time-consuming a function is, we use a method called "Big O notation." We use the Big O notation, a language, to describe how time-consuming an algorithm is. It's how we assess the value of several approaches to an issue, and U supports our decision-making.
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<span>Speak up more so people don't think you're rude</span>
Answer:
Hi!
The answer to:
a. 6 bits.
b. 9 bits.
c. 26 bits.
Explanation:
a. For the states of the U.S.A, you need 50 or more combinations to represents each element.
<u>If you use 6 bits, the possible combinations are 2⁶ = 64. </u>
b. For days in a year, you need 365 or more combinations to represents each element.
<u>If you use 9 bits, the possible combinations are 2⁹ = 512.
</u>
c. For inhabitants of California, you need 36,457,549 or more combinations to represents each element.
<u>If you use 26 bits, the possible combinations are 2²⁶ = 67,108,864.</u> If you use 25 bits instead of 26, then you have 2²⁵ = 33,554,432 combinations. These possible combinations are not enough to represent each inhabitant.
