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The cube of a number $x$ is $x^3=x\cdot x\cdot x$, using the exponent three. For example, $2^3=8$ and $(-2{)}^3=-8$.

For a unit modulo n $a$ and a nonnegative integer $k$, division with remainder writes $k=q\phi (n)+r$ with $0\le r<\phi (n)$. Euler's theorem then givesThis replaces a potentially enormous exponent by a small remainder. The exponent itself is reduced modulo Euler's totient function, while the powers are reduced modulo $n$: these are different integer moduli.

For a worked modulo-fifteen calculation, seven is coprime to fifteen and $\phi (15)=8$. ThereforeUse exponentiation by squaring on the remaining exponent $5=(101{)}_2$, reducing after each multiplication:Thus $7^{2029}\mathrm{mod}15=7$. Modular exponentiation supplies the computation; Euler's theorem supplies the optional shortening of its exponent. Exponentiation by squaring also works for nonunit bases, without using this shortening.

The guaranteed exponent $\phi (n)$ is a common return exponent and an upper bound on the shortest period; it need not be that period. The multiplicative order $d={\mathrm{ord}}_n(a)$ is the least positive return exponent for the chosen unit. The Carmichael function $\lambda (n)$ is the least positive return exponent that works for every unit. Their precise relation isEither smaller return exponent can replace $\phi (n)$ in exponent reduction for units. Modulo fifteen, ${\mathrm{ord}}_{15}(7)=\lambda (15)=4<8=\phi (15)$, so $2029=507\cdot 4+1$ gives $7^{2029}\equiv 7$ immediately. For base four the multiplicative order is only two, smaller even than $\lambda (15)$. A return at exponent eight alone does not prove that the multiplicative order is eight.^[ref]

If the reduced exponent is zero, the rule gives $a^k\equiv a^0=1$, and still requires a unit. Applying it to the nonunit three would falsely predict $3^8\equiv 3^0=1(\mathrm{mod}15)$; the actual residue is six. For negative exponents, first interpret the power using a modular multiplicative inverse, as in modular exponentiation.

For a prime number $p$ and an integer $a$ not divisible by $p$, Fermat's little theorem states the modular congruenceEquivalently, for every integer $a$, including $a<0$ and multiples of $p$,The first formulation follows from the second by multiplying by a modular multiplicative inverse of $a$. Conversely, multiply the first by $a$ when $p\nmid a$; when $p\mid a$, both sides of the second are zero modulo $p$. The divisibility condition in the first formulation is essential.^[ref]

For $p=7$, $3^6=729=7\cdot 104+1$, so $3^6\equiv 1(\mathrm{mod}7)$ and $3^7\equiv 3(\mathrm{mod}7)$. Also $(-2{)}^6=64\equiv 1(\mathrm{mod}7)$, while $7^6\equiv 0(\mathrm{mod}7)$: a zero residue class cannot satisfy the first formulation. It does satisfy $7^7\equiv 7\equiv 0(\mathrm{mod}7)$. A failure at a zero residue class therefore says nothing against a candidate's being a prime number.

The prime number $p=2$ is included. Its only nonzero residue class is one: every odd integer $a$ satisfies $a^{2-1}=a\equiv 1(\mathrm{mod}2)$. Every integer also satisfies $a^2\equiv a(\mathrm{mod}2)$, because $a(a-1)$ is even. Testing the first formulation with an even integer would violate its hypothesis.

The useful surprise is that every nonzero residue class returns to one at the same exponent $p-1$, although its shortest return can be earlier. This gives exponent reduction for units: for a nonnegative integer $k$, $a^k\equiv a^{k\mathrm{mod}(p-1)}(\mathrm{mod}p)$ when $p\nmid a$. It also computes a modular multiplicative inverse as $a^{p-2}$ modulo $p$, including $p=2$. Repeated squaring makes these modular exponentiations practical.

In group theory, the nonzero residue classes form the multiplicative group of integers modulo n with $n=p$, of group order $p-1$. Lagrange's theorem says that the multiplicative order of $a$ divides $p-1$, giving the guaranteed return. Euler's theorem extends this to a general integer modulus $n$, using the units and Euler's totient function $\phi (n)$; the Carmichael function gives the least common return exponent for all units. A nonzero residue class modulo a composite number need not be a unit.

Leonhard Euler's paper presented in 1732 and published in 1738 states a historical formulation: Theorem 1 says that powers with exponent $n-1$ leave zero or one upon division by a prime number $n$. The preceding discussion states the difference-of-powers version and explicitly says that he does not have its proof.^[ref]

To add two finite floating-point numbers, compare their exponents, shift the smaller significand to a common exponent, add or subtract the signed significands, normalize the result, and apply floating-point rounding. Extra low-order bits are needed to determine correct rounding when shifting or cancellation discards information. A carry beyond the leading digit increases the exponent; cancellation can decrease it. Unlike exact real addition, this operation need not be associative. Changing the child evaluation order in a fixed binary expression tree need not change its parenthesization, whereas reassociation changes which intermediate results undergo floating-point rounding.

For $n\ge 1$ and $1\le k\le n$, the displayed recurrence relation uses $G_{n,0}(t)=1$ and $G_{n,k}(t)=0$ when $k<0$ or $k>n$. At $t=1$ it becomes Pascal's identity.

An exact weighted-subset formula isThe exponent measures how far the selected positions lie beyond the earliest possible choices $0,\dots ,k-1$. Split the subsets according to whether they contain $n-1$. Those without it give $G_{n-1,k}(t)$; deleting it from those with it removes exactly $n-k$ from their exponent. This gives the displayed recurrence relation, explaining why it builds polynomials with nonnegative integer coefficients.

A sixteen-bit floating-point representation with one sign bit, five exponent bits and ten stored fraction bits. For sign $s$, stored exponent $E$ and fraction integer $F$, normal finite values areThe implicit leading one gives eleven significant binary digits. With $E=0$, the value is $(-1{)}^{s}F{2}^{-24}$, including signed zero at $F=0$ and subnormal numbers otherwise. The stored exponent $E=31$ is reserved for infinities at $F=0$ and NaN otherwise. The positive normal range is $2^{-14}$ through $65504$; the least positive subnormal number is $2^{-24}$.

On a measure space of positive total measure, the L-infinity norm of an essentially bounded measurable function is $\Vert f{\Vert }_{\infty }={\mathrm{esssup}}_{\mu }|f|$. The absolute value matters: a constant function $f=-3$ has L-infinity norm three, even though its essential supremum is $-3$. The norm belongs to the equivalence class in Lp space; different representatives equal almost everywhere give the same answer. ^[ref].

For the function $g$ in essential infimum and essential supremum, $\Vert g{\Vert }_{\infty }=1$ under Lebesgue measure, whereas its supremum norm is four. Replacing the exceptional values by the ordinary values $x$ leaves every finite Lp norm unchanged as well: $\Vert g{\Vert }_p=({\int }_0^1{x}^{p}dx{)}^{1/p}=(p+1{)}^{-1/p}$. This tends to one as $p\to \infty$. That agreement explains why the endpoint with exponent $p=\infty$ uses an essential bound rather than a pointwise bound.

A finite probability space makes the distinction between zero and small measure explicit. Let $f(a)=1$, $f(b)=3$, and $f(c)=10$. Assign weights $\mu (a)=1/2-\epsilon$, $\mu (b)=1/2$, $\mu (c)=\epsilon$, where $0\le \epsilon <1/2$. For $1\le p<\infty$,When $\epsilon =0$, the singleton $\{c\}$ is a null set and $\Vert f{\Vert }_{\infty }=3$. For every $\epsilon >0$, however small, $\Vert f{\Vert }_{\infty }=10$. For example, $\epsilon =0.001$ gives $\Vert f{\Vert }_1=2.009$ and $\Vert f{\Vert }_2=\sqrt{5.099}$, yet the large-$p$ Lp norms approach ten. The ordinary pointwise supremum norm is ten in both cases.

Why can a rare value dominate? For a bounded function on a probability space, an almost everywhere bound $|f|\le M$ gives $\Vert f{\Vert }_p\le M$. If $0<t<\Vert f{\Vert }_{\infty }$, the set $A_t=\{|f|>t\}$ has positive probability, so $\Vert f{\Vert }_p\ge t\mu (A_t{)}^{1/p}$. As $p\to \infty$, the factor $\mu (A_t{)}^{1/p}$ tends to one. These two bounds squeeze the limit to $\Vert f{\Vert }_{\infty }$. The probability measure assumption keeps total measure finite; this explanation does not assert the same finite-$p$ integrability on arbitrary infinite-measure spaces.

An Lp norm measures the size of a measurable function by averaging a power of its absolute value with respect to a specified measure. For $1\le p<\infty$,when the integral is finite. The exponent $p$ controls how strongly large values influence the result. The Lp space identifies functions equal almost everywhere, so this becomes a norm on the corresponding equivalence classes. For $p=\infty$, use the essential supremum of $|f|$, as defined by the L-infinity norm. ^[ref].

For a two-coordinate vector $(3,4)$, treat the coordinates as a function on two inputs with counting measure. Then $\Vert (3,4){\Vert }_1=3+4=7$, $\Vert (3,4){\Vert }_2=\sqrt{9+16}=5$, and $\Vert (3,4){\Vert }_{\infty }=4$. The middle case is the Euclidean norm; the first adds the coordinate magnitudes, and the last takes their largest value. These are different answers to different questions about size, all satisfying the triangle inequality.

If instead each input has probability $1/2$, the same values give $\Vert f{\Vert }_1=3.5$ and $\Vert f{\Vert }_2=\sqrt{12.5}$, while $\Vert f{\Vert }_{\infty }=4$ remains unchanged. Thus the measure is not an optional label. On a probability space, the finite Lp norms are nondecreasing in $p$ whenever they are finite, by Hölder's inequality; the unnormalized two-coordinate example above decreases instead. Statements comparing different exponents must specify their measure convention.

Why are these sizes useful? If $e$ is an error function, its L1 norm measures aggregate absolute error, its L2 norm measures the square root of aggregate quadratic loss, and its L-infinity norm bounds error almost everywhere. Concentrating a large error on a set of small positive measure can give a modest finite Lp norm but a large L-infinity norm. The three-point example and diagram in L-infinity norm show this explicitly, including why a null set behaves differently from a rare event.

The Lyapunov moment inequality says that higher positive absolute moments give no smaller absolute moment sizes on a probability space. For a real random variable $Z$ and $0<r<s$,allowing an infinite value. For $r\ge 1$ the left expression is an Lp norm; below one it is a quasi-norm, so the usual triangle inequality terminology needs that distinction. The comparison uses a probability measure of total mass one; arbitrary unnormalized measure spaces need a factor depending on their total mass.

For a nonnegative count, the first moment size is its expected value, while the second is the square root of its second moment. Both ten-trial counts in second moment method have first moment size one. The independent count has second moment size $\sqrt{1.9}\approx 1.37840$, whereas the shared-outcome count $N=10B$ has second moment size $\sqrt{10}\approx 3.16228$. The second moment distinguishes models with the same average because its square increases the influence of rare large counts.

More generally, for $N=10B$ with a Bernoulli random variable $B$ of parameter $1/10$,It rises from one at $r=1$ toward ten as $r\to \infty$. Replacing $1/10$ by a parameter $0<q\le 1$ gives $10q^{1/r}$, which decreases toward zero for a fixed positive exponent when $q\downarrow 0$, making the rare-event and large-exponent limits different questions. For a scaled Rademacher random variable taking values $-2$ and two with equal probabilities, every absolute moment size is exactly two: increasing the exponent does not distinguish signs when the absolute value is constant.

For finite $\mathbb{E}|Z{|}^s$, apply the reversed Jensen's inequality to the strictly concave function $t^{r/s}$ and the nonnegative random variable $|Z{|}^s$:Taking the positive $r$th root proves the claim. Equality in this finite case holds exactly when $|Z|$ is constant almost surely. If the higher moment is infinite, the upper bound is infinite and remains valid. Exponent zero is excluded from the statement; logarithmic or geometric limiting summaries require separately stated conventions, especially when $Z$ can vanish.

For a prime number $p$ and $p\nmid a$, multiplication by $a$ gives a permutation of the $p-1$ nonzero residue classes. For $a=3$ modulo seven, the representatives $1,2,3,4,5,6$ map to $3,6,2,5,1,4$: the same collection in a different order. Repeating this permutation from one visits all six representatives before returning.

For $a=2$ modulo seven, the permutation cycles are instead $(1,2,4)$ and $(3,6,5)$. The powers of two visit only the permutation cycle containing one, so their multiplicative order is three. Both permutation cycles return after three steps; applying the permutation six times still fixes every nonzero residue class. Fermat's little theorem guarantees a return at six, not that every base visits every nonzero residue class.

More generally, every permutation cycle of this map has length equal to the multiplicative order of $a$: starting at a nonzero residue class $x$, a return means $a^{k}x\equiv x(\mathrm{mod}p)$, and its modular multiplicative inverse allows cancellation of $x$. Thus the $p-1$ nonzero residue classes split into equal-length permutation cycles, which also explains the divisibility of the multiplicative order into $p-1$.

The product argument gives a short proof outline. Since multiplication by $a$ is a permutation of the nonzero residue classes,Every factor is a unit, so their product has a modular multiplicative inverse and can be cancelled. This leaves $a^{p-1}\equiv 1(\mathrm{mod}p)$. The whole argument works for $p=2$, where the product is just one.^[ref]

In a gravity-dominated excavation model, displaced mass scales as $\rho D^3$ and lifting it through a distance of order $D$ costs order $\rho g{D}^4$. If a comparable fraction of impact energy supplies this work across similar impacts, $\boxed{D\propto E_{\mathrm{impact}}^{1/4}}$. This is a scaling justification, not a universal crater law: strength-dominated small craters or varying coupling efficiencies can have different exponents.

For a normal half-precision floating-point number, write the stored fields as sign $s$, biased exponent $E$ and ten-bit fraction $f$. The value is $(-1{)}^s(1.f{)}_2{2}^{E-15}$ for $1\le E\le 30$. The leading one is implicit: the ten stored fraction bits give eleven significant binary digits including that one. The normalized binary representations and stored fields are

Thus the four 16-bit words areEvery conversion is exact: all four numbers have terminating binary representations within the available precision.

Both signs are positive, so perform addition of the aligned significands. The true exponents are $-1$ and $3$, a difference of four. Shift the smaller significand right four places, retaining its low bits:Add at the common exponent:Renormalization raises the true exponent to $4$ and the biased exponent to $E=19=10011_2$. There are only seven fraction places to retain, so no floating-point rounding changes the result:The complete word is 0100110000001000.

The smallest positive normal half-precision floating-point number uses $E=1$ and a zero fraction, while the largest uses $E=30$ and all ten fraction bits equal to one. HenceThe corresponding words are 0 00001 0000000000 and 0 11110 1111111111. The stored exponent $31$ is reserved and cannot extend the finite normal range.

For a subnormal number, $E=0$ and the implicit leading bit becomes zero, while the scale remains $2^{1-15}=2^{-14}$. The least positive nonzero fraction is $0.0000000001_2=2^{-10}$, givingUsing scale $2^{-15}$ here would be incorrect: the subnormal number convention uses the minimum normal scale, not the ordinary biased-exponent formula at $E=0$.

For the unsigned interpretation of the binary string, the set positions have weights $2^{15},2^{13},2^{12},2^{11},2^{10},2^8,2^6,2^5,2^4$. ThusIn 16-bit two's complement, the most significant bit is set, so subtract $2^{16}$ from that unsigned interpretation:For a half-precision floating-point number, split the fields at their actual boundaries rather than the printed groups of four:The sign is negative, the true exponent is $15-15=0$, and the fraction is $2^{-2}+2^{-4}+2^{-5}+2^{-6}=23/64$. It is a normal finite value, so

For an integer $n$ to be factored, John Pollard's method chooses an integer $a$ coprime to $n$ and an exponent $M$ divisible by many small prime powers. Compute $d=\gcd (a^M-1,n)$ by modular exponentiation and the Euclidean algorithm. If $1<d<n$, it is a proper factor. If a prime divisor $p$ of $n$ has $p-1\mid M$, Fermat's little theorem ensures $p\mid d$; success requires some other prime divisor not to divide $a^M-1$. Common choices are $M=B!$ and $M=\mathrm{lcm}(1,\dots ,B)$. The method exploits a smooth $p-1$, rather than a small factor $p$ itself.

In this positive integer context, a power of two is $2^k$ for a nonnegative integer $k$, giving $1,2,4,8,\dots$. Allowing negative or real exponents gives a broader meaning of exponentiation, which is not intended in the integer divisibility criteria here.

The indicator function example reaches a squared L2 norm ratio of two. To approach four, concentrate more strongly near zero. For $0\le a<1/2$, use the measurable functionAll integrals use Lebesgue measure $dx$. Keeping both portions of the squared L2 norm givesConsequently,Every allowed member has ratio strictly below four. For any proposed constant $C<4$, a sufficiently large $a<1/2$ has ratio greater than $C$. Combined with Hardy's integral inequality, this proves that four is the smallest possible squared L2 norm constant.^[ref]

The endpoint is excluded: at $a=1/2$, ${\int }_0^1|f_a{|}^{2}dx={\int }_0^1{x}^{-1}dx=\infty$, and the inner portion of $\Vert H{f}_a{\Vert }_2^2$ also diverges. Its tail still has finite integral four. There is no finite L2 norm ratio at this endpoint, hence no equality example obtained by substituting it. The limit four is unattained by this family. Multiplying each $f_a$ by $\sqrt{1-2a}$ makes its L2 norm one without changing the ratio.

The significant-digit factor $m$ in a floating-point representation $(-1{)}^{s}m{\beta }^e$, where $s$ is the sign bit, $\beta$ the radix and $e$ the exponent. For a normal positive binary significand, $1\le m<2$, and the leading one need not be stored. In a subnormal number, the leading digit is zero at the minimum normal exponent scale. The significand is a numerical factor, distinct from the stored trailing fraction bits that encode part of it.

For a real exponent $s>1$ and a positive integer $N$, the series truncation error in the defining Riemann zeta function series isThe integral comparison for the positive decreasing function $x^{-s}$ gives the explicit boundsOn each unit interval, the area under $x^{-s}$ lies between its endpoint heights. Summing from the first omitted term gives the lower integral from $N+1$ to infinity and the upper integral from $N$ to infinity. This uses positivity and monotonicity, not an assumption that the last computed term equals the remaining error.

At $N=10$, the bounds are $1/11\le R_{10}(2)\le 1/10$, whereas $1/(3\cdot 11^3)\le R_{10}(4)\le 1/(3\cdot 10^3)$. The errors above fit both intervals. Merely looking at the eleventh term would give $1/121\approx 0.008264$ for squares, far smaller than the actual remaining $0.095166$.

For a chosen absolute error target $\epsilon >0$, the sufficient condition is $N\ge ((s-1)\epsilon {)}^{-1/(s-1)}$, rounded upward to an integer. Thus $\epsilon =10^{-4}$ requires $N=10000$ by this bound for squares but only $N=15$ for fourth powers. This is a guarantee from an upper bound, not a claim that those are the smallest successful term counts.