```
import CalculusWithJulia
using Plots
plotly()
using Polynomials
using RealPolynomialRoots
import SymPy # imported only: some functions, e.g. degree, need qualification
```

# 13 The Polynomials package

This section will use the following add-on packages:

While `SymPy`

can be used to represent polynomials, there are also native `Julia`

packages available for this and related tasks. These packages include `Polynomials`

, `MultivariatePolynomials`

, and `AbstractAlgebra`

, among many others. (A search on juliahub.com found over \(50\) packages matching “polynomial”.) We will look at the `Polynomials`

package in the following, as it is straightforward to use and provides the features we are looking at for univariate polynomials.

## 13.1 Construction

The polynomial expression \(p = a_0 + a_1\cdot x + a_2\cdot x^2 + \cdots + a_n\cdot x^n\) can be viewed mathematically as a vector of numbers with respect to some “basis”, which for standard polynomials, as above, is just the set of monomials, \(1, x, x^2, \dots, x^n\). With this viewpoint, the polynomial \(p\) can be identified with the vector `[a0, a1, a2, ..., an]`

. The `Polynomials`

package provides a wrapper for such an identification through the `Polynomial`

constructor. We have previously loaded this add-on package.

To illustrate, the polynomial \(p = 3 + 4x + 5x^2\) is constructed with

`= Polynomial([3,4,5]) p `

^{2}

where the vector `[3,4,5]`

represents the coefficients. The polynomial \(q = 3 + 5x^2 + 7x^4\) has some coefficients that are \(0\), these too must be indicated on construction, so we would have:

`= Polynomial([3,0,5,0,7]) q `

^{2}+ 7∙x

^{4}

The `coeffs`

function undoes `Polynomial`

, returning the coefficients from a `Polynomial`

object.

`coeffs(q)`

```
5-element Vector{Int64}:
3
0
5
0
7
```

Once defined, the usual arithmetic operations for polynomials follow:

`+ q p `

^{2}+ 7∙x

^{4}

`*q + p^2 p`

^{2}+ 60∙x

^{3}+ 71∙x

^{4}+ 28∙x

^{5}+ 35∙x

^{6}

A polynomial has several familiar methods, such as `degree`

:

`degree(p), degree(q)`

`(2, 4)`

The zero polynomial has degree `-1`

, by convention.

Polynomials may be evaluated using function notation, that is:

`p(1)`

`12`

This blurs the distinction between a polynomial expression – a formal object consisting of an indeterminate, coefficients, and the operations of addition, subtraction, multiplication, and non-negative integer powers – and a polynomial function.

The polynomial variable, in this case `1x`

, can be returned by `variable`

:

`= variable(p) x `

This variable is a `Polynomial`

object, so can be manipulated as a polynomial; we can then construct polynomials through expressions like:

`= (x-2)^2 * (x-1) * (x+1) r `

^{2}- 4∙x

^{3}+ x

^{4}

The product is expanded for storage by `Polynomials`

, which may not be desirable for some uses. A new variable can produced by calling `variable()`

; so we could have constructed `p`

by:

```
= variable()
x 3 + 4x + 5x^2
```

^{2}

A polynomial in factored form, as `r`

above is, can be constructed from its roots. Above, `r`

has roots \(2\) (twice), \(1\), and \(-1\). Passing these as a vector to `fromroots`

re-produces `r`

:

`fromroots([2,2,1,-1])`

^{2}- 4∙x

^{3}+ x

^{4}

The `fromroots`

function is basically the factor theorem which links the factored form of the polynomial with the roots of the polynomial: \((x-k)\) is a factor of \(p\) if and only if \(k\) is a root of \(p\). By combining a factor of the type \((x-k)\) for each specified root, the polynomial can be constructed by multiplying its factors. For example, using `prod`

and a generarator, we would have:

```
= variable()
x prod(x - k for k in [2,2,1,-1])
```

^{2}- 4∙x

^{3}+ x

^{4}

The `Polynomials`

package has different ways to represent polynomials, and a factored form can also be used. For example, the `fromroots`

function constructs polynomials from the specified roots and `FactoredPolynomial`

leaves these in a factored form:

`fromroots(FactoredPolynomial, [2, 2, 1, -1])`

This form is helpful for some operations, for example polynomial multiplication and positive integer exponentiation, but not others such as addition of polynomials, where such polynomials must first be converted to the standard basis to add and are then converted back into a factored form.

The indeterminate, or polynomial symbol is a related, but different concept to `variable`

. Polynomials are stored as a collection of coefficients, an implicit basis, *and* a symbol, in the above this symbol is `:x`

. A polynomial’s symbol is checked to ensure that polynomials with different symbols are not algebraically combined, except for the special case of constant polynomials. The symbol is specified through a second argument on construction:

`= Polynomial([1,2,3], "t") s `

^{2}

As `r`

uses “`x`

”, and `s`

a “`t`

” the two can not be added, say:

`+ s r `

`LoadError: ArgumentError: Polynomials have different indeterminates`

## 13.2 Graphs

Polynomial objects have a plot recipe defined – plotting from the `Plots`

package should be as easy as calling `plot`

:

`plot(r, legend=false) # suppress the legend`

The choice of domain is heuristically identified; and it can be manually adjusted, as with:

`plot(r, 1.5, 2.5, legend=false)`

## 13.3 Roots

The default `plot`

recipe checks to ensure the real roots of the polynomial are included in the domain of the plot. To do this, it must identify the roots. This is done *numerically* by the `roots`

function, as in this example:

```
= variable()
x = x^5 - x - 1
p roots(p)
```

```
5-element Vector{ComplexF64}:
-0.7648844336005849 - 0.35247154603172626im
-0.7648844336005849 + 0.35247154603172626im
0.18123244446987605 - 1.0839541013177107im
0.18123244446987605 + 1.0839541013177107im
1.1673039782614187 + 0.0im
```

A consequence of the fundamental theorem of algebra and the factor theorem is that any fifth degree polynomial with integer coefficients has \(5\) roots, where possibly some are complex. For real coefficients, these complex values must come in conjugate pairs, which can be observed from the output. The lone real root is approximately `1.1673039782614187`

. This value being a numeric approximation to the irrational root.

`SymPy`

also has a `roots`

function. If both `Polynomials`

and `SymPy`

are used together, calling `roots`

must be qualified, as with `Polynomials.roots(...)`

. Similarly, `degree`

is provided in both, so it too must be qualified.

The `roots`

function numerically identifies roots. As such, it is susceptible to floating point issues. For example, the following polynomial has one root with multiplicity \(5\), but \(5\) distinct roots are numerically identified:

```
= variable()
x = (x-1)^5
p roots(p)
```

```
5-element Vector{ComplexF64}:
0.9990471549546425 + 0.0im
0.9997060762327137 - 0.0009060416777928532im
0.9997060762327137 + 0.0009060416777928532im
1.0007703462899709 - 0.0005593477455685818im
1.0007703462899709 + 0.0005593477455685818im
```

The `Polynomials`

package has the `multroot`

function to identify roots of polynomials when there are multiplicities expected. This function is not exported, so is called through:

```
= variable()
x = (x-1)^5
p multroot(p) Polynomials.Multroot.
```

`(values = [1.0], multiplicities = [5], κ = 0.1348399724926484, ϵ = 0.0)`

Floating point error can also prevent the finding of real roots. For example, this polynomial has \(3\) real roots, but `roots`

finds but \(1\), as the two nearby ones are identified as complex:

```
= variable()
x = -1 + 254x - 16129x^2 + x^9
p roots(p)
```

```
9-element Vector{ComplexF64}:
-3.598055712463138 - 1.7316513703737992im
-3.598055712463138 + 1.7316513703737992im
-0.8903404519370794 - 3.890985317737253im
-0.8903404519370794 + 3.890985317737253im
0.007874015748031492 - 2.1956827901729616e-10im
0.007874015748031492 + 2.1956827901729616e-10im
2.486125235439532 - 3.1203279635732817im
2.486125235439532 + 3.1203279635732817im
3.9887938264253116 + 0.0im
```

The `RealPolynomialRoots`

package, loaded at the top of this section, can assist in the case of identifying real roots of square-free polynomials (no multiple roots). For example:

```
= coeffs(-1 + 254x - 16129x^2 + x^9)
ps = ANewDsc(ps)
st refine_roots(st)
```

```
3-element Vector{BigFloat}:
3.988793826425306473637498065677956284273266856074118202991227741350084256244296
0.007874015750717330650219309356068735624266307967208752898078374308955233457932687
0.007874015745345659793212286434490456492725902385145819999560060903342575200075027
```

## 13.4 Fitting a polynomial to data

The fact that two distinct points determine a line is well known. Deriving the line is easy. Say we have two points \((x_0, y_0)\) and \((x_1, y_1)\). The *slope* is then

\[ m = \frac{y_1 - y_0}{x_1 - x_0}, \quad x_1 \neq x_0 \]

The line is then given from the *point-slope* form by, say, \(y= y_0 + m\cdot (x-x_0)\). This all assumes, \(x_1 \neq x_0\), as were that the case the slope would be infinite (though the vertical line \(x=x_0\) would still be determined).

A line, \(y=mx+b\) can be a linear polynomial or a constant depending on \(m\), so we could say \(2\) points determine a polynomial of degree \(1\) or less. Similarly, \(3\) distinct points determine a degree \(2\) polynomial or less, \(\dots\), \(n+1\) distinct points determine a degree \(n\) or less polynomial. Finding a polynomial, \(p\) that goes through \(n+1\) points (i.e., \(p(x_i)=y_i\) for each \(i\)) is called polynomial interpolation. The main theorem is:

Polynomial interpolation theorem: There exists a unique polynomial of degree \(n\) or less that interpolates the points \((x_0,y_0), (x_1,y_1), \dots, (x_n, y_n)\) when the \(x_i\) are distinct.

(Uniqueness follows as suppose \(p\) and \(q\) satisfy the above, then \((p-q)(x) = 0\) at each of the \(x_i\) and is of degree \(n\) or less, so must be the \(0\) polynomial. Existence comes by construction. See the Lagrange basis in the questions.)

Knowing we can succeed, we approach the problem of \(3\) points, say \((x_0, y_0)\), \((x_1,y_1)\), and \((x_2, y_2)\). There is a polynomial \(p = a\cdot x^2 + b\cdot x + c\) with \(p(x_i) = y_i\). This gives \(3\) equations for the \(3\) unknown values \(a\), \(b\), and \(c\):

\[\begin{align*} a\cdot x_0^2 + b\cdot x_0 + c &= y_0\\ a\cdot x_1^2 + b\cdot x_1 + c &= y_1\\ a\cdot x_2^2 + b\cdot x_2 + c &= y_2\\ \end{align*}\]

Solving this with `SymPy`

is tractable. A comprehension is used below to create the \(3\) equations; the `zip`

function is a simple means to iterate over \(2\) or more iterables simultaneously:

```
@syms a b c xs[0:2] ys[0:2]
SymPy.= [a*xi^2 + b*xi + c ~ yi for (xi,yi) in zip(xs, ys)]
eqs = SymPy.solve(eqs, [a,b,c]) abc
```

```
sys:1: SymPyDeprecationWarning:
non-Expr objects in a Matrix is deprecated. Matrix represents
a mathematical matrix. To represent a container of non-numeric
entities, Use a list of lists, TableForm, NumPy array, or some
other data structure instead.
See https://docs.sympy.org/latest/explanation/active-deprecations.html#deprecated-non-expr-in-matrix
for details.
This has been deprecated since SymPy version 1.9. It
will be removed in a future version of SymPy.
```

\[\begin{equation*}\begin{cases}b & \text{=>} &\frac{xs₀^{2} ys₁ - xs₀^{2} ys₂ - xs₁^{2} ys₀ + xs₁^{2} ys₂ + xs₂^{2} ys₀ - xs₂^{2} ys₁}{xs₀^{2} xs₁ - xs₀^{2} xs₂ - xs₀ xs₁^{2} + xs₀ xs₂^{2} + xs₁^{2} xs₂ - xs₁ xs₂^{2}}\\c & \text{=>} &\frac{xs₀^{2} xs₁ ys₂ - xs₀^{2} xs₂ ys₁ - xs₀ xs₁^{2} ys₂ + xs₀ xs₂^{2} ys₁ + xs₁^{2} xs₂ ys₀ - xs₁ xs₂^{2} ys₀}{xs₀^{2} xs₁ - xs₀^{2} xs₂ - xs₀ xs₁^{2} + xs₀ xs₂^{2} + xs₁^{2} xs₂ - xs₁ xs₂^{2}}\\a & \text{=>} &\frac{- xs₀ ys₁ + xs₀ ys₂ + xs₁ ys₀ - xs₁ ys₂ - xs₂ ys₀ + xs₂ ys₁}{xs₀^{2} xs₁ - xs₀^{2} xs₂ - xs₀ xs₁^{2} + xs₀ xs₂^{2} + xs₁^{2} xs₂ - xs₁ xs₂^{2}}\\\end{cases}\end{equation*}\]

As can be seen, the terms do get quite unwieldy when treated symbolically. Numerically, the `fit`

function from the `Polynomials`

package will return the interpolating polynomial. To compare,

`fit(Polynomial, [1,2,3], [3,1,2])`

^{2}

and we can compare that the two give the same answer with, for example:

`.=> [1,2,3])..., (ys .=> [3,1,2])...) abc[b]((xs `

\(- \frac{13}{2}\)

(Ignore the tricky way of substituting in each value of `xs`

and `ys`

for the symbolic values in `x`

and `y`

.)

##### Example Inverse quadratic interpolation

A related problem, that will arise when finding iterative means to solve for zeros of functions, is *inverse* quadratic interpolation. That is finding \(q\) that goes through the points \((x_0,y_0), (x_1, y_1), \dots, (x_n, y_n)\) satisfying \(q(y_i) = x_i\). (That is \(x\) and \(y\) are reversed, as with inverse functions.) For the envisioned task, where the inverse quadratic function intersects the \(x\) axis is of interest, which is at the constant term of the polynomial (as it is like the \(y\) intercept of typical polynomial). Let’s see what that is in general by replicating the above steps (though now the assumption is the \(y\) values are distinct):

```
@syms a b c xs[0:2] ys[0:2]
SymPy.= [a*yi^2 + b*yi + c ~ xi for (xi, yi) in zip(xs,ys)]
eqs = SymPy.solve(eqs, [a,b,c])
abc abc[c]
```

\(\frac{xs₀ ys₁^{2} ys₂ - xs₀ ys₁ ys₂^{2} - xs₁ ys₀^{2} ys₂ + xs₁ ys₀ ys₂^{2} + xs₂ ys₀^{2} ys₁ - xs₂ ys₀ ys₁^{2}}{ys₀^{2} ys₁ - ys₀^{2} ys₂ - ys₀ ys₁^{2} + ys₀ ys₂^{2} + ys₁^{2} ys₂ - ys₁ ys₂^{2}}\)

We can graphically see the result for the specific values of `xs`

and `ys`

as follows:

## 13.5 Questions

###### Question

Do the polynomials \(p = x^4\) and \(q = x^2 - 2\) intersect?

###### Question

Do the polynomials \(p = x^4-4\) and \(q = x^2 - 2\) intersect?

###### Question

How many real roots does \(p = 1 + x + x^2 + x^3 + x^4 + x^5\) have?

###### Question

Mathematically we say the \(0\) polynomial has no degree. What convention does `Polynomials`

use? (Look at `degree(zero(Polynomial))`

.)

###### Question

Consider the polynomial \(p(x) = a_1 x - a_3 x^3 + a_5 x^5\) where

\[\begin{align*} a_1 &= 4(\frac{3}{\pi} - \frac{9}{16}) \\ a_3 &= 2a_1 -\frac{5}{2}\\ a_5 &= a_1 - \frac{3}{2}. \end{align*}\]

- Form the polynomial
`p`

by first computing the \(a\)s and forming`p=Polynomial([0,a1,0,-a3,0,a5])`

- Form the polynomial
`q`

by these commands`x=variable(); q=p(2x/pi)`

The polynomial `q`

, a \(5\)th-degree polynomial, is a good approximation of for the sine function.

Make graphs of both `q`

and `sin`

. Over which interval is the approximation (visually) a good one?

(This blog post shows how this approximation is valuable under some specific circumstances.)

###### Question

The polynomial

`fromroots([1,2,3,3,5])`

^{2}+ 74∙x

^{3}- 14∙x

^{4}+ x

^{5}

has \(5\) sign changes and \(5\) real roots. For `x = variable()`

use `div(p, x-3)`

to find the result of dividing \(p\) by \(x-3\). How many sign changes are there in the new polynomial?

###### Question

The identification of a collection of coefficients with a polynomial depends on an understood **basis**. A basis for the polynomials of degree \(n\) or less, consists of a minimal collection of polynomials for which all the polynomials of degree \(n\) or less can be expressed through a combination of sums of terms, each of which is just a coefficient times a basis member. The typical basis is the \(n+1\) polynomials \(1, x, x^2, \dots, x^n\). However, though every basis must have \(n+1\) members, they need not be these.

A basis used by Lagrange is the following. Let there be \(n+1\) points distinct points \(x_0, x_1, \dots, x_n\). For each \(i\) in \(0\) to \(n\) define

\[ l_i(x) = \prod_{0 \leq j \leq n; j \ne i} \frac{x-x_j}{x_i - x_j} = \frac{(x-x_1)\cdot(x-x_2)\cdot \cdots \cdot (x-x_{i-1}) \cdot (x-x_{i+1}) \cdot \cdots \cdot (x-x_n)}{(x_i-x_1)\cdot(x_i-x_2)\cdot \cdots \cdot (x_i-x_{i-1}) \cdot (x_i-x_{i+1}) \cdot \cdots \cdot (x_i-x_n)}. \]

That is \(l_i(x)\) is a product of terms like \((x-x_j)/(x_i-x_j)\) *except* when \(j=i\).

What is is the value of \(l_0(x_0)\)?

Why?

What is the value of \(l_i(x_i)\)?

What is the value of \(l_0(x_1)\)?

Why?

What is the value of \(l_i(x_j)\) *if* \(i \ne j\)?

Suppose the \(x_0, x_1, \dots, x_n\) are the \(x\) coordinates of \(n+1\) distinct points \((x_0,y_0)\), \((x_1, y_1), \dots, (x_n,y_n).\) Form the polynomial with the above basis and coefficients being the \(y\) values. That is consider:

\[ p(x) = \sum_{i=0}^n y_i l_i(x) = y_0l_0(x) + y_1l_1(x) + \dots + y_nl_n(x) \]

What is the value of \(p(x_j)\)?

This last answer is why \(p\) is called an *interpolating* polynomial and this question shows an alternative way to identify interpolating polynomials from solving a system of linear equations.

###### Question

The Chebyshev (\(T\)) polynomials are polynomials which use a different basis from the standard basis. Denote the basis elements \(T_0\), \(T_1\), … where we have \(T_0(x) = 1\), \(T_1(x) = x\), and for bigger indices \(T_{i+1}(x) = 2xT_i(x) - T_{i-1}(x)\). The first others are then:

\[\begin{align*} T_2(x) &= 2xT_1(x) - T_0(x) = 2x^2 - 1\\ T_3(x) &= 2xT_2(x) - T_1(x) = 2x(2x^2-1) - x = 4x^3 - 3x\\ T_4(x) &= 2xT_3(x) - T_2(x) = 2x(4x^3-3x) - (2x^2-1) = 8x^4 - 8x^2 + 1 \end{align*}\]

With these definitions what is the polynomial associated to the coefficients \([0,1,2,3]\) with this basis?

The `Polynomials`

package has an implementation, so you can check your answer through `convert(Polynomial, ChebyshevT([0,1,2,3]))`

. Similarly, the `SpecialPolynomials`

package has these and many other polynomial bases represented.

The `ApproxFun`

package is built on top of polynomials expressed in this basis, as the Chebyshev polynomials have special properties which make them very suitable when approximating functions with polynomials. The `ApproxFun`

package uses easier-to-manipulate polynomials to approximate functions very accurately, thereby being useful for investigating properties of non-linear functions leveraging properties for polynomials.