This post spawned from work I've been doing on the SICMUtils library; I've just released 0.13.0 and I hope you'll give it a try.

SICMUtils is the engine behind the wonderful "Structure and Interpretation of Classical Mechanics", an advanced physics textbook by Gerald Sussman of SICP fame. I'm trying to get the textbook running in the browser as a Clojurescript library, and as part of that effort I've had to re-implement quite a bit of numerical code in Clojure.

Can I admit that I've gone deeper than necessary? After I'd gotten to know the library, I noted that the power series implementation could use some work. Jack Rusher pointed me at a gorgeous power series implementation by Doug McIlroy (my Uncle's boss at Bell Labs!). See his website for 10-lines version of beautiful Haskell.

Doug's paper, "Power Series, Power Serious", goes into more detail on the implementation, which leans hard on Haskell's lazy evaluation. Clojure has lazy sequences too! I implemented the paper here, then went further and made power series executable, and extended them to play with the whole generic arithmetic system in this namespace.

This post is a rendered, literate programming version of `impl.cljc`

, the core power series implementation based on Doug's work. It's exactly the same code we use in the library.

Enjoy!

The following code builds up a power series implementation that backs two `sicmutils`

types:

`Series`

, which represents a generic infinite series of arbitrary values, and`PowerSeries`

, a series that represents a power series in a single variable; in other words, a series where the nth entry is interpreted as the coefficient of \(x^n\):

\begin{equation}

\label{eq:4}

[a\ b\ c\ d\ ...] = a + bx + cx^2 + dx^3 + ...

\end{equation}

We'll proceed by building up implementations of the arithmetic operations `+`

, `-`

, `*`

, `/`

and a few others using bare Clojure lazy sequences.

The implementation follows Doug McIlroy's beautiful paper, "Power Series, Power Serious". Doug also has a 10-line version in Haskell on his website.

Let's go!

# Sequence Operations

A 'series' is an infinite sequence of numbers, represented by Clojure's lazy sequence. First, a function `->series`

that takes some existing sequence, finite or infinite, and coerces it to an infinite seq by concatenating it with an infinite sequence of zeros. (We use `v/zero-like`

so that everything plays nicely with generic arithmetic.)

```
(defn ->series
"Form the infinite sequence starting with the supplied values. The
remainder of the series will be filled with the zero-value
corresponding to the first of the given values."
[xs]
(lazy-cat xs (repeat (v/zero-like (first xs)))))
```

```
(take 10 (->series [1 2 3 4]))
```

```
(1 2 3 4 0 0 0 0 0 0)
```

The core observation we'll use in the following definitions (courtesy of McIlroy) is that a power series \(F\) in a variable \(x\):

\begin{equation}

F(x)=f_{0}+x f_{1}+x^{2} f_{2}+\cdots

\end{equation}

Decomposes into a head element \(f_0\) plus a tail series, multiplied by \(x\):

\begin{equation}

\label{eq:3}

F(x) = F_0(x) = f_0 + x F_1(x)

\end{equation}

We'll use this observation to derive the more complicated sequence operations below.

## Negation

To negate a series, negate each element:

```
(defn negate [xs]
(map g/negate xs))
```

Example:

```
(take 7 (negate (->series [1 2 3 4])))
```

```
(-1 -2 -3 -4 0 0 0)
```

## Addition

We can derive series addition by expanding the series \(F\) and \(G\) into head and tail and rearranging terms:

\begin{equation}

\label{eq:5}

F+G=\left(f+x F_{1}\right)+\left(g+xG_{1}\right)=(f+g)+x\left(F_{1}+G_{1}\right)

\end{equation}

This is particularly straightforward in Clojure, where `map`

already merges sequences elementwise:

```
(defn seq:+ [f g]
(map g/+ f g))
```

```
(take 5 (seq:+ (range) (range)))
```

```
(0 2 4 6 8)
```

A constant is a series with its first element populated, all zeros otherwise. To add a constant to another series we only need add it to the first element. Here are two versions, constant-on-left vs constant-on-right:

```
(defn c+seq [c f]
(lazy-seq
(cons (g/+ c (first f)) (rest f))))
(defn seq+c [f c]
(lazy-seq
(cons (g/+ (first f) c) (rest f))))
```

```
(let [series (->series [1 2 3 4])]
[(take 6 (seq+c series 10))
(take 6 (c+seq 10 series))])
```

```
[(11 2 3 4 0 0) (11 2 3 4 0 0)]
```

## Subtraction

Subtraction comes for free from the two definitions above:

```
(defn seq:- [f g]
(seq:+ f (negate g)))
```

```
(take 5 (seq:- (range) (range)))
```

```
(0 0 0 0 0)
```

We *should* get equivalent results from mapping `g/-`

over both sequences, and in almost all cases we do… but until we understand and fix this bug that method would return different results.

Subtract a constant from a sequence by subtracting it from the first element:

```
(defn seq-c [f c]
(lazy-seq
(cons (g/- (first f) c) (rest f))))
```

```
(take 5 (seq-c (range) 10))
```

```
(-10 1 2 3 4)
```

To subtract a sequence from a constant, subtract the first element as before, but negate the tail of the sequence:

```
(defn c-seq [c f]
(lazy-seq
(cons (g/- c (first f)) (negate (rest f)))))
```

```
(take 5 (c-seq 10 (range)))
```

```
(10 -1 -2 -3 -4)
```

## Multiplication

What does it mean to multiply two infinite sequences? As McIlroy notes, multiplication is where the lazy-sequence-based approach really comes into its own.

First, the simple cases of multiplication by a scalar on either side of a sequence:

```
(defn seq*c [f c] (map #(g/mul % c) f))
(defn c*seq [c f] (map #(g/mul c %) f))
```

To multiply sequences, first recall from above that we can decompose each sequence \(F\) and \(G\) into a head and tail.

Mutiply the expanded representations out and rearrange terms:

\begin{equation}

\label{eq:6}

F \times G=\left(f+x F_{1}\right) \times\left(g+x G_{1}\right)=f g+x\left(f G_{1}+F_{1} \times G\right)

\end{equation}

\(G\) appears on the left and the right, so use an inner function that closes over \(g\) to simplify matters, and rewrite the above definition in Clojure:

```
(defn seq:* [f g]
(letfn [(step [f]
(lazy-seq
(let [f*g (g/mul (first f) (first g))
f*G1 (c*seq (first f) (rest g))
F1*G (step (rest f))]
(cons f*g (seq:+ f*G1 F1*G)))))]
(step f)))
```

This works just fine on two infinite sequences:

```
(take 10 (seq:* (range) (->series [4 3 2 1])))
```

```
(0 4 11 20 30 40 50 60 70 80)
```

NOTE This is also called the "Cauchy Product" of the two sequences. The description on the Wikipedia page has complicated index tracking that simply doesn't come in to play with the stream-based approach. Amazing!

## Division

The quotient \(Q\) of \(F\) and \(G\) should satisfy:

\begin{equation}

\label{eq:7}

F = Q \times G

\end{equation}

From McIlroy, first expand out \(F\), \(Q\) and one instance of \(G\):

\begin{equation}

\begin{aligned}

f+x F_{1} &=\left(q+x Q_{1}\right) \times G \cr

&=q G+x Q_{1} \times G=q\left(g+x G_{1}\right)+x Q_{1} \times G \cr

&=q g+x\left(q G_{1}+Q_{1} \times G\right)

\end{aligned}

\end{equation}

Look at just the constant terms and note that \(q = \frac{f}{g}\).

Consider the terms multiplied by \(x\) and solve for \(Q_1\):

\begin{equation}

\label{eq:8}

Q_1 = \frac{(F_1 - qG_1)}{G}

\end{equation}

There are two special cases to consider:

- If \(g=0\), \(q = \frac{f}{g}\) can only succeed if \(f=0\); in this case, \(Q = \frac{F_1}{G1}\), from the larger formula above.
- If \(f=0\), \(Q_1 = \frac{(F_1 - 0 G_1)}{G} = \frac{F_1}{G}\)

Encoded in Clojure:

```
(defn div [f g]
(lazy-seq
(let [f0 (first f) fs (rest f)
g0 (first g) gs (rest g)]
(cond (and (v/nullity? f0) (v/nullity? g0))
(div fs gs)
(v/nullity? f0)
(cons f0 (div fs g))
(v/nullity? g0)
(u/arithmetic-ex "ERROR: denominator has a zero constant term")
:else (let [q (g/div f0 g0)]
(cons q (-> (seq:- fs (c*seq q gs))
(div g))))))))
```

A simple example shows success:

```
(let [series (->series [0 0 0 4 3 2 1])]
(take 5 (div series series)))
```

```
(1 0 0 0 0)
```

## Reciprocal

We could generate the reciprocal of \(F\) by dividing \((1, 0, 0, ...)\) by \(F\). Page 21 of an earlier paper by McIlroy gives us a more direct formula.

We want \(R\) such that \(FR = 1\). Expand \(F\):

\begin{equation}

\label{eq:9}

(f + xF_1)R = 1

\end{equation}

Solve for R:

\begin{equation}

\label{eq:10}

R = \frac{1}{f} (1 - x(F_1 R))

\end{equation}

A recursive definition is no problem in the stream abstraction:

```
(defn invert [f]
(lazy-seq
(let [finv (g/invert (first f))
F1*Finv (seq:* (rest f) (invert f))
tail (c*seq finv (negate F1*Finv))]
(cons finv tail))))
```

This definition of `invert`

matches the more straightforward division implementation:

```
(let [series (iterate inc 3)]
(= (take 5 (invert series))
(take 5 (div (->series [1]) series))))
```

```
true
```

An example:

```
(let [series (iterate inc 3)]
[(take 5 (seq:* series (invert series)))
(take 5 (div series series))])
```

```
[(1N 0N 0N 0N 0N) (1 0 0 0 0)]
```

Division of a constant by a series comes easily from our previous multiplication definitions and `invert`

:

```
(defn c-div-seq [c f]
(c*seq c (invert f)))
```

It's not obvious that this works:

```
(let [nats (iterate inc 1)]
(take 6 (c-div-seq 4 nats)))
```

```
(4 -8 4 0 0 0)
```

But we can recover the initial series:

```
(let [nats (iterate inc 1)
divided (c-div-seq 4 nats)
seq-over-4 (invert divided)
original (seq*c seq-over-4 4)]
(take 5 original))
```

```
(1N 2N 3N 4N 5N)
```

To divide a series by a constant, divide each element of the series:

```
(defn seq-div-c [f c]
(map #(g// % c) f))
```

Division by a constant undoes multiplication by a constant:

```
(let [nats (iterate inc 1)]
(take 5 (seq-div-c (seq*c nats 2) 2)))
```

```
(1 2 3 4 5)
```

## Functional Composition

To compose two series \(F(x)\) and \(G(x)\) means to create a new series \(F(G(x))\). Derive this by substuting \(G\) for \(x\) in the expansion of \(F\):

\begin{equation}

\begin{aligned}

F(G)&=f+G \times F_{1}(G) \cr

&=f+\left(g+x G_{1}\right) \times F_{1}(G) \cr

&=\left(f+g F_{1}(G)\right)+x G_{1} \times F_{1}(G)

\end{aligned}

\end{equation}

For the stream-based calculation to work, we need to be able to calculate the head element and attach it to an infinite tail; unless \(g=0\) above the head element depends on \(F_1\), an infinite sequence.

If \(g=0\) the calculation simplifies:

\begin{equation}

\label{eq:12}

F(G)=f + x G_{1} \times F_{1}(G)

\end{equation}

In Clojure, using an inner function that captures \(G\):

```
(defn compose [f g]
(letfn [(step [f]
(lazy-seq
;; NOTE I don't understand why we get a StackOverflow if I move
;; this assertion out of the ~letfn~.
(assert (zero? (first g)))
(let [[f0 & fs] f
gs (rest g)
tail (seq:* gs (step fs))]
(cons f0 tail))))]
(step f)))
```

Composing \(x^2 = (0, 0, 1, 0, 0, ...)\) should square all $x$s, and give us a sequence of only even powers:

```
(take 10 (compose (repeat 1)
(->series [0 0 1])))
```

```
(1 0 1 0 1 0 1 0 1 0)
```

## Reversion

The functional inverse of a power series \(F\) is a series \(R\) that satisfies \(F(R(x)) = x\).

Following McIlroy, we expand \(F\) (substituting \(R\) for \(x\)) and one occurrence of \(R\):

\begin{equation}

\label{eq:13}

F(R(x))=f+R \times F_{1}(R)=f+\left(r+x R_{1}\right) \times F_{1}(R)=x

\end{equation}

Just like in the composition derivation, in the general case the head term depends on an infinite sequence. Set \(r=0\) to address this:

\begin{equation}

\label{eq:14}

f+x R_{1} \times F_{1}(R)=x

\end{equation}

For this to work, the constant \(f\) must be 0 as well, hence

\begin{equation}

\label{eq:15}

R_1 = \frac{1}{F_1(R)}

\end{equation}

This works as an implementation because \(r=0\). \(R_1\) is allowed to reference \(R\) thanks to the stream-based approach:

```
(defn revert [f]
{:pre [(zero? (first f))]}
(letfn [(step [f]
(lazy-seq
(let [F1 (rest f)
R (step f)]
(cons 0 (invert
(compose F1 R))))))]
(step f)))
```

An example, inverting a series starting with 0:

```
(let [f (cons 0 (iterate inc 1))]
(take 5 (compose f (revert f))))
```

```
(0 1 0 0 0)
```

## Series Calculus

Derivatives of power series are simple and mechanical:

\begin{equation}

\label{eq:16}

D(a x^n) = aD(x^n) = a n x^{n-1}

\end{equation}

Implies that all entries shift left by 1, and each new entry gets multiplied by its former index (ie, its new index plus 1).

```
(defn deriv [f]
(map g/* (rest f) (iterate inc 1)))
```

```
(take 6 (deriv (repeat 1)))
```

```
(1 2 3 4 5 6)
```

Which of course we interpret as

\begin{equation}

\label{eq:17}

1 + 2x + 3x^2 + ...

\end{equation}

The definite integral \(\int_0^{x}F(t)dt\) is similar. To take the anti-derivative of each term, move it to the right by appending a constant term onto the sequence and divide each element by its new position:

```
(defn integral
([s] (integral s 0))
([s constant-term]
(cons constant-term
(map g/div s (iterate inc 1)))))
```

With a custom constant term:

```
(take 6 (integral (iterate inc 1) 5))
```

```
(5 1 1 1 1 1)
```

By default, the constant term is 0:

```
(take 6 (integral (iterate inc 1)))
```

```
(0 1 1 1 1 1)
```

## Exponentiation

Exponentiation of a power series by some integer is simply repeated multiplication. The implementation here is more efficient the iterating `seq:*`

, and handles negative exponent terms by inverting the original sequence.

```
(defn expt [s e]
(letfn [(expt [base pow]
(loop [n pow
y (->series [1])
z base]
(let [t (even? n)
n (quot n 2)]
(cond
t (recur n y (seq:* z z))
(zero? n) (seq:* z y)
:else (recur n (seq:* z y) (seq:* z z))))))]
(cond (pos? e) (expt s e)
(zero? e) (->series [1])
:else (invert (expt s (g/negate e))))))
```

We can use `expt`

to verify that \((1+x)^3\) expands to \(1 + 3x + 3x^2 + x^3\):

```
(take 5 (expt (->series [1 1]) 3))
```

```
(1 3 3 1 0)
```

## Square Root of a Series

The square root of a series \(F\) is a series \(Q\) such that \(Q^2 = F\). We can find this using our calculus methods from above:

\begin{equation}

\label{eq:18}

D(F) = 2Q D(Q)

\end{equation}

or

\begin{equation}

\label{eq:19}

D(Q) = \frac{D(F)}{2Q}

\end{equation}

When the head term of \(F\) is nonzero, ie, \(f \neq 0\), the head of \(Q = \sqrt{F}\) must be \(\sqrt{f}\) for the multiplication to work out.

Integrate both sides:

\begin{equation}

\label{eq:20}

Q = \sqrt{f} + \int_0^x \frac{D(F)}{2Q}

\end{equation}

One optimization appears if the first two terms of \(F\) vanish, ie, \(F=x^2F_2\). In this case \(Q = 0 + x \sqrt{F_2}\).

Here it is in Clojure:

```
(defn sqrt [[f1 & [f2 & fs] :as f]]
(if (and (v/nullity? f1)
(v/nullity? f2))
(cons f1 (sqrt fs))
(let [const (g/sqrt f1)
step (fn step [g]
(lazy-seq
(-> (div (deriv g)
(c*seq 2 (step g)))
(integral const))))]
(step f))))
```

And a test that we can recover the naturals:

```
(let [xs (iterate inc 1)]
(take 6 (seq:* (sqrt xs)
(sqrt xs))))
```

```
(1 2 3 4 5 6)
```

We can maintain precision of the first element is the square of a rational number:

```
(let [xs (iterate inc 9)]
(take 6 (seq:* (sqrt xs)
(sqrt xs))))
```

```
(9 10N 11N 12N 13N 14N)
```

We get a correct result if the sequence starts with \(0, 0\):

```
(let [xs (concat [0 0] (iterate inc 9))]
(take 6 (seq:* (sqrt xs)
(sqrt xs))))
```

```
(0 0 9 10N 11N 12N)
```

# Examples

Power series computations can encode polynomial computations. Encoding \((1-2x^2)^3\) as a power series returns the correct result:

```
(take 10 (expt (->series [1 0 -2]) 3))
```

```
(1 0 -6 0 12 0 -8 0 0 0)
```

Encoding \(\frac{1}{(1-x)}\) returns the power series $1 + x + x^{2} + …$ which sums to that value in its region of convergence:

```
(take 10 (div (->series [1])
(->series [1 -1])))
```

```
(1 1 1 1 1 1 1 1 1 1)
```

\(\frac{1}{(1-x)^2}\) is the derivative of the above series:

```
(take 10 (div (->series [1])
(-> (->series [1 -1])
(expt 2))))
```

```
(1 2 3 4 5 6 7 8 9 10)
```

# Various Power Series

With the above primitives we can define a number of series with somewhat astonishing brevity.

\(e^x\) is its own derivative, so \(e^x = 1 + e^x\):

```
(def expx
(lazy-seq
(integral expx 1)))
```

This bare definition is enough to generate the power series for \(e^x\):

```
(take 10 expx)
```

```
(1 1 1/2 1/6 1/24 1/120 1/720 1/5040 1/40320 1/362880)
```

\(sin\) and \(cos\) afford recursive definitions. \(D(sin) = cos\) and \(D(cos) = -sin\), so (with appropriate constant terms added) on:

```
(declare cosx)
(def sinx (lazy-seq (integral cosx)))
(def cosx (lazy-seq (c-seq 1 (integral sinx))))
```

```
(take 10 sinx)
```

```
(0 1 0 -1/6 0 1/120 0 -1/5040 0 1/362880)
```

```
(take 10 cosx)
```

```
(1 0 -1/2 0 1/24 0 -1/720 0 1/40320 0)
```

tangent and secant come easily from these:

```
(def tanx (div sinx cosx))
(def secx (invert cosx))
```

Reversion lets us define arcsine from sine:

```
(def asinx (revert sinx))
(def atanx (integral (cycle [1 0 -1 0])))
```

These two are less elegant, perhaps:

```
(def acosx (c-seq (g/div 'pi 2) asinx))
(def acotx (c-seq (g/div 'pi 2) atanx))
```

The hyperbolic trig functions are defined in a similar way:

```
(declare sinhx)
(def coshx (lazy-seq (integral sinhx 1)))
(def sinhx (lazy-seq (integral coshx)))
(def tanhx (div sinhx coshx))
(def asinhx (revert sinhx))
(def atanhx (revert tanhx))
(def log1-x
(integral (repeat -1)))
;; https://en.wikipedia.org/wiki/Mercator_series
(def log1+x
(integral (cycle [1 -1])))
```

# Generating Functions

## Catalan numbers

These are a few more examples from McIlroy's "Power Serious" paper, presented here without context.

```
(def catalan
(lazy-cat [1] (seq:* catalan catalan)))
```

```
(take 10 catalan)
```

```
(1 1 2 5 14 42 132 429 1430 4862)
```

ordered trees…

```
(declare tree' forest' list')
(def tree' (lazy-cat [0] forest'))
(def list' (lazy-cat [1] list'))
(def forest' (compose list' tree'))
```

```
(take 10 tree')
```

```
(0 1 1 2 5 14 42 132 429 1430)
```

The catalan numbers again!

```
(def fib (lazy-cat [0 1] (map + fib (rest fib))))
```

See here for the recurrence relation: https://en.wikipedia.org/wiki/Binomial_coefficient#Multiplicative_formula

```
(defn binomial* [n]
(letfn [(f [acc prev n k]
(if (zero? n)
acc
(let [next (/ (* prev n) k)
acc' (conj! acc next)]
(recur acc' next (dec n) (inc k)))))]
(persistent!
(f (transient [1]) 1 n 1))))
```

```
(defn binomial
"The coefficients of (1+x)^n"
[n]
(->series (binomial* n)))
```

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