Big O: Clarifying Prime Obsession Concepts

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In summary, John discusses the concept of Big O in his book Prime Obsession. He explains that if Function A is trapped within + and - of Function B, then F(A) is Big O of F(B). This means that for any constant multiple of F(A), the same relation holds. For example, y=Sin x is Big O of Y = 1 and y=2sinx is also Big O of Y=1, even though it breaks the limit an infinite amount of times. This is because the definition of Big O focuses on the behavior of the function as x approaches infinity, rather than at a specific point.
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hi,

I am reading Prime Obsession and john talks about Big o. He goes if Function A is trapped within + and - of function B then F(A) is Big O of F(B)

So, y=Sin x is Big O of Y = 1 right?

He also goes that if F(A) is Big O of F(B) then so is any multiple of F(A).

so y=2sinx is also big O of Y=1? Even though is breaks the limit infinite amount of times?

Any clarification appreciated. Thanks!
 
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  • #2
Yes, so the definition is not: "A is trapped between + and - the value of B", but "A is trapped between + and - a multiple of the value of B".

Technically speaking, we say that
[tex]f(x) = \mathcal O(g(x)) \qquad (x \to \infty)[/tex]
if there exists some number M > 0 and some value x0 such that
[tex]x > x_0 \implies |f(x)| < M |g(x)|[/tex]

If you have f(x) = c sin(x), then you can easily show that f(x) = O(c), where c is the constant function which takes value c everywhere.
Note that even g(x) = c sin(x) / xn is O(c) (for all n > 1), because for x > 1 we have |g(x)| < |f(x)| < c.

Analogously to the definition of limit as x goes to infinity vs. limit as x goes to some finite number a, we can also define
[tex]f(x) = \mathcal O(g(x)) \qquad (x \to a)[/tex]
if there exists some number M > 0 and some value [itex]\delta > 0[/itex] such that
[tex]|x - a| < \delta \implies |f(x)| < M |g(x)|[/tex]
 
  • #3
oh yeh, forgot about that. Thanks! :D
 

What is "Big O" in mathematics?

"Big O" is a mathematical notation used to describe the rate of growth of a function. It represents the upper bound or worst-case scenario for the time complexity of an algorithm.

What is the significance of "Big O" in computer science?

In computer science, "Big O" is used to analyze the efficiency of algorithms. It helps determine how long an algorithm will take to run and how it will perform as the input size increases.

What is the difference between "Big O", "Big Omega", and "Big Theta"?

"Big O" represents the upper bound or worst-case scenario for time complexity, "Big Omega" represents the lower bound or best-case scenario, and "Big Theta" represents the average case scenario for time complexity.

How is "Big O" used in real-world applications?

"Big O" is used in real-world applications to optimize algorithms and improve the efficiency of software. It helps developers choose the most efficient algorithm for a given task and can also be used to predict how a system will perform under different conditions.

What is the "Prime Obsession" in relation to "Big O"?

The "Prime Obsession" is a concept in mathematics that refers to the fascination with prime numbers and their distribution. In relation to "Big O", it is used to analyze the time complexity of algorithms that involve prime numbers.

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