Big-O Notation Question: Polynomial with Variable Coefficients

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In summary, the conversation discusses expressing a polynomial in Big-O notation, taking into account the variables β and x. The speaker suggests using the definition found on Wikipedia and shows how to apply it in this case. The other person mentions considering β as a constant and x as a variable, and also suggests looking at the series as a geometric series to find a closed form solution.
  • #1
mnb96
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Hello,

I have a polynomial having the form:

[tex]\beta x^5 + \beta^2 x^7 + \beta^3 x^9 + \ldots = \sum_{n=1}^{+\infty}\beta^n x^{2n+3}[/tex]

How can I express this with Big-O notation?
Please, note that I consider β as another variable (independent from x). I already know that if β was a constant I could express the above quantity as [itex]O(x^5)[/itex].
 
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  • #3
First of all I forgot to mention that [itex]x\geq 0[/itex] and [itex]\beta \in \mathbb{R}[/itex].

I will try to apply the definition found in Wikipedia, although that definition refers specifically to functions of one-variable. I am not sure we can use that definition, but I will try.

Let's "pretend" that β is a constant and x the variable. We have: [tex]f(x)=\beta x^5 + \beta^2 x^7 + \beta^3 x^9\ldots[/tex]

and I am interested in studying the behavior for [itex]x\to 0[/itex].
We have that: [tex]|\beta x^5 + \beta^2 x^7 + \beta^3 x^9\ldots | \leq |\beta| x^5 + |\beta^2| x^7 + |\beta^3| x^9\ldots \leq |\beta| x^5 + |\beta^2| x^5 + |\beta^3| x^5 \ldots [/tex], hence we have [itex]f(x)\in O(r^5)[/itex], as expected.

By considering β variable, and x constant we have [itex]f(\beta) \in O(\beta)[/itex].

Now what?
 
  • #4
So you want x->0 and [itex]\beta \to 0[/itex] right?

Your series looks a lot like a geometric series. In fact
[tex]\sum_{i=1}^\infty \beta^nx^{3+2n} = x^3\sum_{i=1}^\infty (\beta x^2)^n[/tex]
For small enough [itex]\beta[/itex] and x you should get a nice closed form from which you can more easily see the series' asymptotic behavior.
 
  • #5


Hello,

Thank you for your question. In order to express this polynomial in Big-O notation, we need to consider the highest degree term in the polynomial. In this case, the highest degree term is β^n x^{2n+3}. Since β is considered a variable independent from x, we can treat it as a constant. Therefore, the highest degree term can be written as O(β^n x^{2n+3}).

Now, we need to determine the value of n that will result in the highest degree term. We can see that as n increases, the degree of the polynomial also increases. Therefore, we can say that the polynomial is of order O(β^n x^{2n+3}) where n is the highest possible value.

In other words, we can express this polynomial in Big-O notation as O(β^n x^{2n+3}), where n is the highest possible value. This notation indicates that the polynomial is of the same order as β^n x^{2n+3}, and any lower order terms can be ignored.

I hope this helps to clarify how to express a polynomial with variable coefficients in Big-O notation. Let me know if you have any further questions.

Best,
 

FAQ: Big-O Notation Question: Polynomial with Variable Coefficients

What is Big-O notation?

Big-O notation is a mathematical notation used to describe the complexity of an algorithm. It represents the worst-case scenario runtime of an algorithm in terms of the input size.

Why is Big-O notation important?

Big-O notation helps us analyze the efficiency of algorithms and compare them to determine which one is more optimal for a given problem. It also allows us to predict the performance of an algorithm as the input size grows.

How is Big-O notation calculated?

Big-O notation is calculated by looking at the number of operations an algorithm performs as the input size grows. The notation ignores constant factors and smaller terms, focusing only on the dominant term that determines the algorithm's overall complexity.

What is the difference between O(1) and O(n) time complexity?

O(1) time complexity means that an algorithm's runtime remains constant, regardless of the input size. On the other hand, O(n) time complexity means that the algorithm's runtime increases linearly with the input size. In other words, the time taken to execute an O(n) algorithm doubles as the input size doubles.

Can Big-O notation be used for space complexity?

Yes, Big-O notation can also be used to measure the space complexity of an algorithm. It represents the maximum amount of memory an algorithm requires to solve a problem as the input size grows.

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