What is the meaning of M(n) in algorithm complexity analysis?

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In algorithm complexity analysis, O(n^2 M(n)) indicates that the complexity is proportional to n^2 multiplied by the function M(n). The notation M(n) represents an additional function that modifies the growth rate of the overall complexity. When analyzing functions f(n) and g(n), if f(n) is O(g(n)), it means there exist constants c and n0 such that f(n) is bounded by c times g(n) for sufficiently large n. For example, if f(n) = 5n and g(n) = n, then f(n) can be shown to be O(g(n)) with appropriate constants. Understanding these relationships helps clarify how M(n) influences the overall complexity.
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I'm trying to teach myself some algorithm complexity and I've run into a problem. I'm starting to understand about O and o notation and big theta notation. I've run into notations like O(n^2 M(n)). Does this mean that the complexity is n^2 times whatever M(n) means? (Natural next question) what does M(n) mean?
 
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Let f(n) and g(n) be two functions (since you mentioned algorithms I assume f(n) and g(n) are only positive, e.g. f(n) and g(n) stand for run times). Let's say we have the relationship f=O(g(n)). This means there are constants c and n0 such that:

f(n) \leq c \cdot g(n) for all n \geq n_0

Example:
f(n)=5*n
g(n)=n

We have to show that there are constants c and n0 such that:

5 n \leq c \cdot n for all n \geq n_0

This is the case for c=5 and n0=1. Thus,
f(n)=O(g(n)) or
5*n=O(n)

It shouldn't be a problem to translate this to your complexitiy O(n^2 M(n)) where M(n) seems to be some function.
 
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