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An algorithm is a finite, precise procedure for transforming an input into an output.

Here "finite" refers to the description: a short algorithm can perform arbitrarily many steps on increasingly large inputs. Solving a computational problem additionally requires termination and the specified output on every permitted input. State assumptions explicitly: binary search requires a sorted array.

An algorithm is an idea that can have implementations in different programming languages. Its explanation should make three things visible: the permitted inputs, the rule for each step, and the meaning of the returned output. Algorithm correctness then separates finding the right answer from eventually finishing; time complexity asks how much work finishing requires.

A particularly useful design question is: what knowledge survives each step? In binary search, it is the location of every possible match inside the remaining candidate range. Writing that fact as a loop invariant turns an intuitive shortcut into a procedure whose discarded candidates can be justified. This often helps discover the algorithm, rather than merely check it afterward.

Partial correctness means that, if the algorithm terminates on a permitted input, its output meets the specification. Total correctness additionally requires termination on every permitted input.

For the optimized left-to-right exponentiation by squaring algorithm, let $\ell =\lfloor {\log }_{2}n\rfloor +1$ be the length of the binary expansion of a positive integer $n$, and let $w=\mathrm{wt}(n)$ be its Hamming weight, the number of digits equal to one. There are exactly $\ell -1$ digits after the leading one, and exactly $w-1$ remaining ones. Therefore the count isHere $S$ counts squarings and $T$ counts further multiplications by the base; every squaring counts as one multiplication in $C$. Assignment, digit reading, and any inversion are outside this count. The formula includes $n=1$, which requires zero multiplications; define $C(0)=0$ separately.

For a fixed length $\ell$, the bounds are $\ell -1\le C(n)\le 2\ell -2$. A power of two reaches the lower bound because its Hamming weight is one; $n=2^{\ell }-1$ reaches the upper bound because all digits are one. For $n=13$, $\ell =4$ and $w=3$, giving $C(13)=5$. Thus the arithmetic operation count is $O(\log (n+1))$ in Big O notation, but inputs of similar magnitude can have different counts.

This is a count for the specified algorithm, not a claim that it always minimizes multiplications among all methods. For instance, at $n=15$ the count is six, but the powers with exponents $1,2,3,6,12,15$ can be built in five multiplications: square, multiply by $a$, square, square, then multiply by the already stored $a^3$. Saving operations this way uses a different schedule and stored intermediate powers.

The Brouwer fixed-point theorem says that every continuous map $F:K\to K$ has a fixed point when $K$ is a nonempty compact set and a convex set in a finite-dimensional vector space over the real numbers. Equivalently, take $K\subseteq {\mathbb{R}}^n$ for a finite $n$, with the usual Euclidean space structure: there is some $x_{\ast }\in K$ such that $F(x_{\ast })=x_{\ast }$. The conclusion guarantees at least one fixed point, without asserting uniqueness or telling us how quickly to find one. ^[ref]

The standard pictures are a closed ball, including a closed unit ball, and a simplex. A filled disk qualifies; its bounding circle alone does not. A probability simplex also qualifies even though its dimension is smaller than that of its ambient Euclidean space. The hypotheses have concrete meanings: being a convex set keeps every line segment between allowed points inside $K$, and compactness prevents escape or loss of a limiting point. In Euclidean space, the Heine-Borel theorem identifies compact sets with closed sets that are bounded sets. Crucially, every output must belong to the same $K$ as the input.

For a fully computable example, let $D=\{(u,v):u^2+v^2\le 1\}$ be the filled unit disk. Apply a counterclockwise quarter-turn rotation, shrink by $0.4$, and add a translation:This affine map is a continuous map. Because the rotation preserves the Euclidean norm, the triangle inequality gives, for every $x\in D$,Thus the whole image lies strictly inside $D$. More precisely, it is a disk of radius $0.4$ centred at $(0.3,0.1)$. This containment check is what makes the Brouwer fixed-point theorem applicable; a formula being a continuous map by itself would not suffice.

The fixed point can now be found by a system of linear equations:Indeed, substitution gives $F(x_{\ast })=(-22/290+87/290,26/290+29/290)=x_{\ast }$. Its Euclidean norm is $\sqrt{290}/58\approx 0.293610$, safely inside $D$. The figure shows arrows from sample inputs to their outputs; an arrow disappears at the fixed point because the displacement $F(x)-x$ vanishes there.

Repeated iterations of a map happen to succeed for this example. Starting at $x_0=(1,0)$ and setting $x_{k+1}=F(x_k)$ gives $x_1=(0.3,0.5)$, $x_2=(0.1,0.22)$, and $x_3=(0.212,0.14)$. Subtracting the fixed point equation yields $x_{k+1}-x_{\ast }=0.4R_{90}(x_k-x_{\ast })$, so the Euclidean distance to $x_{\ast }$ shrinks by exactly $0.4$ each step. This extra fact follows from the contraction mapping property and the Banach fixed-point theorem. It is additional information beyond the Brouwer fixed-point theorem.

The distinction becomes visible with two simpler continuous maps of the same disk. The pure rotation $G(u,v)=(-v,u)$ has exactly one fixed point, $(0,0)$, since $u=-v$ and $v=u$ force both to vanish. Yet iterations of a map starting at $(1,0)$ here cycle through $(0,1)$, $(-1,0)$, and $(0,-1)$ forever. Their Euclidean distance from $(0,0)$ stays one, so there is no convergence in a metric space. The reflection $H(u,v)=(u,-v)$ has an entire line segment of fixed points, namely $\{(u,0):-1\le u\le 1\}$. Starting off that line segment produces an alternating pair. Existence, uniqueness, and convergence of iterations of a map are separate questions.

Removing hypotheses changes the answer. Restrict the same quarter-turn rotation to the circle $S^1=\{(u,v):u^2+v^2=1\}$. It remains a continuous map into that compact set, but its only possible fixed point, $(0,0)$, has been removed. The circle is not a convex set. Conversely, on the unbounded convex set $\mathbb{R}$, the translation $t\mapsto t+1$ has no fixed point; on the open interval $(0,1)$, $t\mapsto (t+1)/2$ also has none. Neither domain is a compact set. The finite-dimensional closed ball statement should likewise not be extended to arbitrary infinite-dimensional vector spaces, where a closed ball need not be a compact set.

The one-dimensional version explains the connection to the intermediate value theorem. For a continuous map $f:[a,b]\to [a,b]$, put $h(t)=f(t)-t$. Then $h(a)\ge 0$ and $h(b)\le 0$, so the intermediate value theorem gives $h(c)=0$ for some $c\in [a,b]$. In higher dimensions, the Brouwer fixed-point theorem supplies a corresponding existence principle when this scalar sign argument is unavailable. Its practical value is ensuring that a proposed equation $x=F(x)$ has a solution once a suitable domain is found; it supplies no general promise of a fast algorithm.

A useful application is positive matrix eigenvector from simplex normalization: rescale the output of a strictly positive matrix to make its entries sum to one. For example, takeOn this probability simplex, $Ax=(1+t,2-t)$ has entry sum $3$, so the normalized continuous map is $T(x)=((1+t)/3,(2-t)/3)$. Both entries are positive and sum to one, keeping the image inside the probability simplex. Its fixed point equation is $t=(1+t)/3$, hence $t=1/2$. Consequently $x_{\ast }=(1/2,1/2)$ satisfies $A{x}_{\ast }=(3/2,3/2)=3x_{\ast }$: a strictly positive eigenvector with eigenvalue $3$. In larger dimensions, the Brouwer fixed-point theorem guarantees the corresponding positive eigenvector without requiring us to solve a system of linear equations in advance. The fixed point expresses preservation of relative weights, rather than preservation of their unnormalized size.

Proofs can be approached through the no-retraction theorem or Sperner's lemma. In the latter approach, increasingly fine triangulations locate approximate fixed points; compactness supplies a convergent subsequence, and continuous maps turn its limit into an exact fixed point. ^[ref]

A primary historical reference is L. E. J. Brouwer's 1911 paper Über Abbildung von Mannigfaltigkeiten, whose digitized original is hosted by Zenodo under DOI 10.1007/BF01456812. This gives a direct route to the early literature behind the Brouwer fixed-point theorem. ^[ref]

A numerical value retained for reuse by memoization. It is usable only while the spreadsheet formula and all its relevant inputs remain valid. After changing A1 from 3 to 5, following outgoing directed edges finds exactly B1,B2,C1,D1. Invalidate their cached cell values before evaluating another requested result; install the new input value for A1. The cached cell values of A2 and E1 remain valid because neither has a directed path from A1.

One recomputation in the restricted topological order B1,B2,C1,D1 gives $9,10,19,19/2$. Four spreadsheet formulas are recomputed, while E1 stays at 16. Removing invalid values from the cache prevents a subtle error: a finished marker from the previous evaluation does not prove that the old value is correct after an edit. The affected set is conservative; a changed prerequisite might leave a consumer's value numerically equal, but this simple scheme still invalidates it. For a formula edit, update the incoming directed edges and corresponding outgoing consumer lists as well as invalidating the edited spreadsheet cell and its downstream consumers. Microsoft's documentation explicitly describes both direct and indirect dependents being marked for recalculation.^[ref]

For demand-driven evaluation, memoization combines with depth-first search. Each spreadsheet cell has one of three states: UNSEEN means no valid cached cell value; ACTIVE means its evaluation is on the current call stack; FINISHED means a valid value is available. Initialize valid input spreadsheet cells as FINISHED. Start with all spreadsheet formulas UNSEEN, or retain only valid FINISHED entries after invalidation. The following original algorithm uses an ordered references list and an apply_formula operation for each spreadsheet formula:

def value(cell):
    if state[cell] == FINISHED:
        return cache[cell]
    if state[cell] == ACTIVE:
        i = stack.index(cell)
        raise CircularReference(stack[i:] + [cell])
    state[cell] = ACTIVE
    stack.append(cell)
    try:
        args = [value(p) for p in references[cell]]
        cache[cell] = apply_formula(cell, args)
        state[cell] = FINISHED
        return cache[cell]
    except Exception:
        state[cell] = UNSEEN
        raise
    finally:
        stack.pop()

Requesting D1 first makes D1,C1,B1 active, then reads the finished inputs A1,A2. B1 finishes at 7. Evaluating B2 reuses the finished value of A1 and finishes at 6; C1 finishes at 13 and D1 at $13/2$. A later request for D1 immediately returns its cached cell value. The shared prerequisite A1 is not a directed cycle: it was finished before the second request. The crucial distinction is between a previously visited vertex and a still-active vertex. The exceptional path restores unfinished states while unwinding the call stack, so failed evaluations are not cached as successful results. MIT's depth-first search notes use stack-duration marks to distinguish an edge to an active ancestor and to detect a directed cycle.^[ref]

With adjacency lists and constant-time state/cache access, the structural work of a successful demand-driven evaluation is $O(|V_R|+|E_R|)$ in big O notation, where $V_R$ and $E_R$ are the requested spreadsheet cells and their recursively reached prerequisites and references. Each reached spreadsheet formula is computed once; each reference is inspected once. This time complexity counts dependency bookkeeping separately from the cost of the arithmetic operations. Detecting a spreadsheet circular reference can additionally scan the current call stack once to report its witness.

Catastrophic cancellation occurs when subtraction of nearly equal approximations removes their matching leading digits and exposes earlier roundoff error. The subtraction can itself be exact on the stored operands: the information may already have been lost while obtaining them. Goldberg distinguishes this from cancellation of accurately known quantities, which can be harmless.^[ref]

For $x>0$, consider the single real-valued functionThe rationalization of a square-root difference proves equality in exact arithmetic. The two evaluations use the same input $x$, but the direct version first forms a value near one and then subtracts one. The rearranged version keeps the small input in its numerator and divides by a denominator near two.

For binary64 x = 1e-16, the rounded sum 1.0 + x is one, so sqrt(1.0 + x) - 1.0 is zero. The rationalized evaluation returns approximately $5\times 10^{-17}$. A 100-digit Decimal calculation using the exact stored input gives relative errors of one and about $2.50\times 10^{-17}$ respectively. At x = 1e-12, the direct result is about $5.0004445029\times 10^{-13}$, with relative error $8.89\times 10^{-5}$; the rearranged result is about $4.99999999999875\times 10^{-13}$, with relative error $1.21\times 10^{-17}$.

The plot uses Decimal.from_float(x) to preserve the binary input exactly, computes $x/(\sqrt{1+x}+1)$ at 100 decimal digits, and verifies the reference at 160 digits. Exact conversion of a stored binary value is documented by Python.^[ref] Each error is $|\hat{f}-f|/|f|$, with the computed result converted exactly to Decimal before comparison. Hence the curves measure evaluation error for one common input, rather than a mixture of input conversion error and evaluation error. The plotted interval $10^{-16}\le x\le 10^{-1}$ is far above the subnormal number range.

The original problem is well conditioned with respect to relative changes in $x$. Using the derivative $f^{\prime }(x)=1/(2\sqrt{1+x})$ and rationalization of a square-root difference, its relative condition number isIt tends to one as $x$ tends to zero. The direct evaluation instead introduces the intermediate difference $s-1$ with $s=\sqrt{1+x}$; an error of order unit roundoff in $s$ is large relative to $f(x)\approx x/2$. This is a failure of the chosen algorithm's accuracy despite a small condition number for the same real-valued function. The rationalized evaluation avoids that intermediate catastrophic cancellation, retaining relative accuracy near the unit roundoff scale throughout the plotted interval. Recognizing this structure can improve a calculation far more than merely printing extra digits.

Chaikin subdivision generates a rounded subdivision curve by repeatedly cutting the corners off a control polygon. Each edge contributes two new control points, one a quarter of the way along the edge and the other three quarters of the way along it. Connecting these new control points in edge order replaces the old corners by shorter line segments. This makes Chaikin subdivision a useful way to draw a smooth outline from a small, editable set of control points, without evaluating B-spline basis functions at each drawing step. Joy presents both the corner-cutting rule and its closed version.^[ref]

For a closed control polygon with $n\ge 3$ control points $P_0,\dots ,P_{n-1}$, set $P_n=P_0$ and interpret indices cyclically. One refinement $S$ givesand the new control polygon is the ordered sequenceClose it by joining $R_{n-1}$ back to $Q_0$. Use the old control points for every average in the same refinement; updating them in place would define a different algorithm. One refinement produces $2n$ stored control points, and $k$ refinements produce $2^{k}n$, counting entries even if some coordinates coincide. Open control polygons need an explicit endpoint convention: applying only the interior edge rule discards the original endpoints, whereas retaining endpoints changes the boundary rule. The closed analysis below uses cyclic indices throughout.

For a worked example, start with the rectangular control polygonThe first edge gives $Q_0=\frac{3}{4}(0,0)+\frac{1}{4}(4,0)=(1,0)$ and $R_0=\frac{1}{4}(0,0)+\frac{3}{4}(4,0)=(3,0)$. Applying Chaikin subdivision to all four edges, including the closing edge, gives exactly eight control points in this order:Thus the rectangle becomes an octagonal control polygon. Two refinements have $16$ control points, and five have $128$. The illustration shows finite control polygons at stages $0,1,2,5$; the last is already visually rounded but still consists of line segments.

The convex hull property follows directly from the weights. Each new control point is a convex combination of the endpoints of one old edge, because the weights are nonnegative and sum to one. Consequently, if $H_k$ is the convex hull of the stage-$k$ control points, then $H_{k+1}\subseteq H_k$. Every later line segment, and every limit point, stays in the original convex hull. This gives a useful geometric enclosure. It also explains why Chaikin subdivision is approximating rather than an interpolation rule: the rectangle's four original corners are outside the first octagon's convex hull, so the limiting curve cannot pass through them. A refinement control polygon generally lies off the limiting curve as well.

For the cyclic uniform rule, the closed limit is the periodic quadratic uniform B-spline curve determined by the original control points. Uniform refers to equal spacing in the spline knot sequence, not equal physical lengths of edges of the control polygon. An infinite uniform control sequence has the corresponding local description; arbitrary open endpoint conventions need separate treatment. Joy derives the quarter-point refinement by splitting quadratic uniform B-spline spans.^[ref]

Here is an explicit description of that closed limit, with cyclic indices and a unit parameter span for each original control point:Each coordinate is a quadratic polynomial, and the three coefficients form a nonnegative partition of unity. The span runs from $(P_{i-1}+P_i)/2$ to $(P_i+P_{i+1})/2$. Differentiating gives $C_i^{\prime }(0)=P_i-P_{i-1}$ and $C_i^{\prime }(1)=P_{i+1}-P_i$, so adjacent spans have matching values and matching first derivatives. Their second derivatives generally differ. Thus the parameterized curve is $C^1$, generally not $C^2$; degenerate control points can still make its tangent vector vanish. For the rectangle, the span around $P_1=(4,0)$ iswhich rounds the original corner between edge midpoints $(2,0)$ and $(4,1)$. For equally spaced abscissae, the centered Chaikin basis function gives the scalar graph version of this quadratic B-spline representation and has support of width three original grid intervals.

Another useful property is affine equivariance of a geometric basis: refining and applying an affine map commute. For $T(x)=Ax+b$,and the same calculation holds for $R_i$. The sum-to-one weights account for the translation. Therefore $T(S(P))=S(T(P))$ at every refinement, and also for the limit. A designer can apply a shear mapping, translation, or other affine map to the original control polygon and obtain exactly the transformed outline.

For example, compose the shear mapping $(x,y)\mapsto (x+y/2,y)$ with the translation $(1,-1)$ to obtain $T(x,y)=(x+y/2+1,y-1)$. The rectangle's control points become $(1,-1),(5,-1),(6,1),(2,1)$. On its right edge, cutting first gives $Q_1=(4,1/2)$ and then $T(Q_1)=(21/4,-1/2)$. Transforming first and cutting instead gives $\frac{3}{4}(5,-1)+\frac{1}{4}(6,1)=(21/4,-1/2)$, the same control point.

Historically, George M. Chaikin published “An algorithm for high-speed curve generation” in Computer Graphics and Image Processing 3(4), December 1974, pages 346–349. Its emphasis was practical curve generation through a recursive algorithm using simple arithmetic, with applications including machine control and three-dimensional curves. The lasting appeal of Chaikin subdivision is how an elementary local average creates an entire smooth B-spline curve.^[ref]

A clique in a simple graph $G$ is a set $C$ of vertices such that every pair of distinct members is adjacent. Equivalently, its induced subgraph $G[C]$ is a complete graph. The surrounding graph need not itself be complete: a clique describes complete pairwise connectivity within a selected group.

Consider the original graph withThe set $\{a,b,c\}$ is a clique because all three pairs $ab,ac,bc$ are edges. The set $\{b,c,d\}$ is not a clique: the edge $bd$ is missing. A path in a graph connecting all selected vertices is therefore insufficient; every pair must have a direct edge.

Here the clique number is $3$. The maximum clique is $\{a,b,c\}$, but $\{c,d\}$ and $\{d,e\}$ are also maximal cliques. This distinction is useful when an algorithm stops as soon as its current clique cannot be enlarged: that stopping condition establishes maximality, while finding the largest possible clique is a separate requirement.

Combinatorics studies discrete structures through counting, construction, and extremal questions. Its objects include subsets, permutations, graphs, and set partitions. The question is often easy to ask: how many teams can we choose, how many arrangements avoid a collision, or how few connections are enough? The challenge is to organize the possibilities so that every permitted object is counted exactly as intended.

For example, choosing two people from five gives ten teams. Choosing a captain and deputy from those same five gives twenty assignments. The difference is whether order is part of the object: a team is a subset, while the two named roles distinguish an ordered pair. The binomial coefficient and the rule of product encode these two counting problems. Choosing the right objects comes before manipulating a formula.

The beauty is that a change of viewpoint can turn enumeration into an explanation. Double counting connects two descriptions of one collection; Pascal's identity splits a selection into cases; the inclusion-exclusion principle repairs overlapping counts. These methods also supply exact finite models for probability and help measure the possibilities an algorithm must handle. Richard Stanley's textbook introduces the subject through counting questions and then develops these general tools. ^[ref]

A colouring is computable when an algorithm sorts the finite input subset, computes its colour and terminates. Its colour classes are uniformly computable sets. The distinction between existence of an infinite homogeneous set for a colouring and effective construction of one is a basic phenomenon in computability theory.

Computer science studies how information can be represented and transformed by algorithms, and what those transformations can achieve. Its central questions connect mathematics to practical design: what answer should a procedure produce, why is it correct, and how much work does it require as its input grows? Computability theory asks which tasks can be solved at all; computational complexity theory asks what resources a solution needs.

The beauty is that choosing a representation can change what is feasible. Finding a name among a million entries sounds like a million checks. Store the entries in a sorted array, and binary search needs at most twenty middle-entry comparisons. The order makes each unsuccessful comparison eliminate a whole region of possibilities. A data structure supplies useful organization; an algorithm exploits it; time complexity explains the resulting savings.

Algorithm correctness covers every permitted input, including absent names and empty collections. Time complexity predicts scaling; statistical program profiling investigates a particular implementation.

Conway's Game of Life is a cellular automaton in which a simple local rule produces patterns that remain still, pulse, or travel across a square grid. Choose the starting pattern; after that there are no player decisions. The Life blinker and Life glider below make the surprise concrete: the rule mentions only births and survival, yet repeated applications create a clock and a moving shape.

The standard board is infinite, with cells at ordered pairs $(x,y)\in {\mathbb{Z}}^2$ of integers. At generation $t\in \{0,1,2,\dots \}$ let $a_t(x,y)$ be $1$ for a live cell and $0$ for a dead cell. Count all eight cells sharing an edge or a corner, excluding the center itself:The local Boolean function defining the next generation isThis is the rule B3/S23: a dead cell is born with exactly three live neighbors; a live cell survives with exactly two or three. A live cell with zero or one, or with four through eight, dies. A dead cell with two stays dead. All counts and center states come from generation $t$: this is a synchronous update, even if an algorithm computes its cells one at a time.^[ref]

In particular, the two births in the Life blinker do not help any other cell survive until the following generation. Writing each answer over its old value and then reading that new value for another count changes the rule. The simplest correct algorithm reads one array and writes the next generation into a second array; swap their roles after the entire step. The one-row in-place Life update achieves the same rule with less auxiliary storage by preserving the old values that later counts still need.

A finite implementation must specify its boundary convention. Holding all cells outside the rectangle permanently dead can destroy a Life glider when it reaches the edge. Wrapping neighbor coordinates from one edge to the opposite edge allows interaction with patterns on the other side. Neither convention is the infinite board. The figures display finite windows into an infinite board initially dead everywhere except the stated live cells; the windows do not impose walls.

Conway's Game of Life illustrates emergence in a precise, modest sense: the Life glider's motion is a collective pattern derived from local birth and death, without a motion command. It has deterministic evolution; visually surprising behavior introduces no random choices. Nor do the words "alive" and "dead" make it an experimentally validated model of biology. The beauty is that the same rule yields a stable four-cell square, the Life blinker, and the Life glider: three qualitatively different behaviors from a handful of cells.

For the stable square, take the live set $\{(0,0),(1,0),(0,1),(1,1)\}$. Each live cell has three live neighbors, and every dead cell has at most two. The next generation is identical: this configuration is a fixed point of the global update function. In contrast, the Life blinker returns after two generations, and the Life glider returns only after allowing a translation.

The patterns are useful as small, exact checks of an algorithm: the square checks survival without births, the Life blinker checks births and deaths in one simultaneous step, and the Life glider checks several steps and diagonal neighbors. More elaborate constructions can encode data and assemble Boolean circuits; Johnston and Greene develop constructions that simulate arbitrary computation in their chapter 9. This is a statement about suitably designed patterns with enough space and time, not a claim that every starting pattern computes something useful or that a fixed finite board has unlimited computer memory.^[ref]

Martin Gardner introduced Conway's Game of Life to his Scientific American readers in October 1970, reporting that Conway selected the rules after experimentation with patterns. The aim was sustained, varied behavior rather than immediate extinction or trivially demonstrable unlimited growth. These were mathematical explorations of chosen rules.^[ref]

Gardner's hands-on procedure is still a useful experiment: place black counters for the current live cells; mark doomed counters by stacking another black counter; mark births with white counters; finish checking before removing the doomed stacks and turning white counters black. Count only the original black occupants. Repeat with three adjacent counters to obtain the Life blinker, then with the five coordinates of the Life glider. Recording each generation tests whether the procedure implements the rule; it does not measure a natural population.^[ref]

The radix-two Cooley--Tukey algorithm recursively splits a discrete Fourier transform into transforms of the even-indexed and odd-indexed samples. For a power-of-two length this evaluates the transform in $O(m\log m)$ arithmetic operations.

The saving comes from reusing the two shorter discrete Fourier transforms for both halves of the output, through an FFT butterfly. For $N=2^p$, each level of the even--odd fast Fourier transform recursion needs work proportional to $N$, and there are $p={\log }_{2}N$ levels. This gives the $O(N\log N)$ time complexity without changing the discrete Fourier transform being computed.

James W. Cooley and John W. Tukey published An Algorithm for the Machine Calculation of Complex Fourier Series in Mathematics of Computation 19 (1965), pp. 297–301. Their original paper factors a composite length as $N=r_1\cdots r_p$, derives an operation count $N(r_1+\cdots +r_p)$, and discusses powers of two and in-place storage. Its opening explicitly builds on earlier algorithms by I. J. Good; the 1965 paper is a landmark computational formulation, rather than the invention of the underlying Fourier analysis. Its positive-exponent convention also illustrates why a Fast Fourier transform's exponent sign must be specified.^[ref]