\( \def\R{\mathbb{R}} \)So, as Google Summer of Code started on Monday, May 25th it is time to give a proper motivation for the project I have proposed. The working title of my project is ** (Multivariate) Asymptotic Expressions**, and its overall goal is to bring

*asymptotic expressions*to SageMath.

### What are A*symptotic Expressions?*

A motivating answer for this question comes from the theory of Taylor series. Assume that we have a sufficiently nice (in this case meaning smooth) function $f : \R \to \R$ that we want to approximate in a neighborhood of some point $x_0 \in \R$. Taylor’s theorem allows us to write $f(x) = T_n(x) + R_n(x)$ where

\[ T_n(x) = \sum_{j=0}^n \frac{f^{(j)}(x_0)}{j!}\cdot (x-x_0)^j = f(x_0) + f'(x_0)\cdot (x-x_0) + \cdots + \frac{f^{(n)}(x_0)}{n!}\cdot (x-x_0)^n, \]

and $R_n(x) = \frac{f^{(n+1)}(\xi)}{(n+1)!} \cdot (x-x_0)^{n+1}$, where $\xi$ lies in a neighborhood of $x_0$. Note that for $x\to x_0$, $R_n(x)$ “behaves like” $(x-x_0)^{n+1}$. In particular, we can certainly find a constant $C > 0$ such that $|R_n(x)| \leq C\cdot |x-x_0|^{n+1}$, or, in other words: for $x\to x_0$ the growth of the function $R_n(x)$ is bounded from above by the growth of $(x-x_0)^{n+1}$.

The idea of bounding the growth of a function by the growth of another function when the argument approaches some number (or $\infty$) is the central idea behind the *big O notation. *For function $f, g : \R \to \R$ we write $f(x) = O(g(x))$ for $x\to x_0$ if there is a constant $C > 0$ such that $|f(x)| \leq C\cdot |g(x)|$ for all $x$ in some neighborhood of $x_0$.

A case that is particularly important is the case of $x_0 = \infty$, that is if we want to compare and/or characterize the behavior of some function for $x\to\infty$, which is also called the functions *asymptotic behavior*. For example, consider the functions $\log x$, $x^3$ and $e^x$. All of them are growing unbounded for $x\to\infty$ — however, their asymptotic behavior differs. This can be seen by considering pairwise quotients of these functions: $\frac{x^3}{e^x} \to 0$ for $x\to\infty$, and therefore the asymptotic growth of $x^3$ can be bounded above by the growth of $e^x$, meaning $x^3 = O(e^x)$ for $x\to\infty$.

The analysis of a functions asymptotic behavior is important for many applications, for example when determining time and space complexity of algorithms in computer science, or for describing the growth of classes of combinatorial objects: take, for example, binary strings of length $2n$ that contain equally many zeros and ones. If $s_n$ denotes the number of such strings, then we have

\[ s_n = \binom{2n}{n} = \frac{4^n}{\sqrt{n\pi}} \left(1 + O\left(\frac{1}{n}\right)\right) \quad\text{ for } n\to\infty. \]

Expressions like these are *asymptotic expressions.* When we consider asymptotic expressions in only one variable, everything works out nicely as a total order is induced. But as soon as multiple variables are involved, we don’t have a total order any more. Consider, for example, $x^2 y$ and $xy^2$ when $x$ and $y$ approach $\infty$. These two elements cannot be compared to each other, which complicates computing with these expressions as they may contain multiple “irreducible” O-terms.

The following univariate and multivariate examples shall demonstrate how computing with such expressions looks like (all variables are assumed to go to $\infty$):

\[ x + O(x) = O(x),\quad x^2 \cdot (x + O(1)) = x^3 + O(x^2),\quad O(x^2) \cdot O(x^3) = O(x^5), \]

\[ x y + O(x^2 y) = O(x^2y),\quad (y \log y + O(y)) (x^2 y + O(4^x \sqrt{x})) = x^2 y^2 \log y + O(x^2 y^2) + O(4^x \sqrt{x} y \log y). \]

Our plan is to provide an implementation based on which computations with these and more complicated expressions are possible.

### Planned Structure

There are four core concepts of our implementation.

**Asymptotic Growth Groups**:*growth elements*like $x^2$, $\log x$, $2^x \cdot x \cdot \log x$. For starters, only univariate power growth groups will be implemented.

**Asymptotic Term Monoids**: These monoids contain*asymptotic terms*— in essence, these are summands of asymptotic terms. Apart from exact term monoids (growth elements with a coefficient), we will also implement O-term monoids as well as a term monoid for a deviation of O-terms. Asymptotic terms have (in addition to their group operation, multiplication)*absorption*as an additional operation: for example, O-terms are able to absorb all asymptotically “smaller” elements.

**Mutable Poset**: As we have mentioned above, due to the fact that multivariate asymptotic expressions do not have a total order with respect to their growth, we need a partially ordered set (“Poset”) that deals with this structure such that operations like absorbing terms can be performed efficiently. The mutable poset is the central data structure that asymptotic expressions are built upon.

**Asymptotic Ring**: This is our top-level structure which is also supposed to be the main interaction object for users. The asymptotic ring contains the asymptotic expressions, i.e. intelligently managed sums of asymptotic terms. All common operations shall be possible here. Furthermore, the interface should be intelligent enough such that admissible expressions from the symbolic ring can be directly converted into elements of the asymptotic ring.

Obviously, this “planned structure” is rather superficial. However, this is only to supplement the motivation for my project with some ideas on the implementation. I’ll go a lot more into the details of what I am currently implementing in the next few blog posts!

## Pingback:

Computing with Asymptotic Expressions | Benjamin Hackl