# Discrete Mathematics Definitions : Algorithm, Searching algorithm, Greedy algorithm, Composite, Prime, Relatively prime integers, Matrix, Matrix addition, Symmetric, Fundamental Theorem of Arithmetic

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On the following terms could you please give my an English text description - in your own words. Thanks.

1. Algorithm:

2. Searching algorithm:

3. Greedy algorithm:

4. Composite:

5. Prime:

6. Relatively prime integers:

7. Matrix:

8. Matrix addition:

9. Symmetric:

10. Fundamental Theorem of Arithmetic:

11. Euclidean Algorithm:

12. Chinese Remainder Theorem:

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On the following terms could you please give my an English text description - in your own words. Thanks.

1. Algorithm:

Informally speaking, an algorithm is a collection of

simple instructions for carrying out a task.

2. Searching algorithm:

In computer science, a search algorithm, broadly speaking, is an algorithm that takes a problem as input and returns a solution to the problem, usually after evaluating a number of possible solutions. Most of the algorithms studied by computer scientists that solve problems are kinds of search algorithms. The set of all possible solutions to a problem is called the search space.

3. Greedy algorithm:

An algorithm that always takes the best immediate, or local, solution while finding an answer. Greedy algorithms find the overall, or globally, optimal solution for some optimization problems, but may find less-than-optimal solutions for some instances of other problems.

For example, if we want to find how many quarters, dimes and pennies can be used to represent a given ...

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- BSc , Wuhan Univ. China
- MA, Shandong Univ.

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- "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
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