Showing posts with label o4-mini-high. Show all posts
Showing posts with label o4-mini-high. Show all posts

Thursday, August 7, 2025

Euclidean algorithm halts with all-and-only commensurable inputs

show: the euclidean algorithm halts if, and only if, it is given commensurable quantities

Let $a,b>0$ be two magnitudes (or lengths). “Commensurable” means there is a third magnitude $d>0$ and positive integers $m,n$ with

$$ a = m d,\quad b = n d, $$

i.e., $a$ and $b$ are integer multiples of a common measure $d$. The Euclidean algorithm (in the modern form using division with remainder, or equivalently the subtractive form) produces a sequence of remainders

$$ r_0 := a,\quad r_1 := b,\quad r_{i+1} := r_{i-1}\bmod r_i, $$

and halts exactly when some $r_k$ divides $r_{k-1}$ giving $r_{k+1}=0$; the last nonzero remainder is then a common measure.

We prove the equivalence:

(⇒) If the Euclidean algorithm halts on $a,b$, then $a$ and $b$ are commensurable. Suppose it halts in finitely many steps with last nonzero remainder $r_k$, so that $r_{k-1} = q_{k} r_k$ and the next remainder is zero. By the division relations backwards,

$$ r_k\mid r_{k-1},\quad r_k\mid r_{k-2},\ \dots,\ r_k\mid r_1=b,\quad\text{and }r_k\mid r_0=a. $$

Thus $a = M r_k,\ b = N r_k$ with integers $M,N$; so $r_k$ is a common measure and $a,b$ are commensurable.

(⇐) If $a$ and $b$ are commensurable, say $a = m d,\ b = n d$ with $m,n\in\mathbb{Z}_{>0}$, then the Euclidean algorithm on $a,b$ reduces to the usual integer Euclidean algorithm on $m,n$ with unit $d$: each division step

$$ r_{i-1} = q_i r_i + r_{i+1} $$

when scaled by $d^{-1}$ becomes an integer division among the corresponding integers. Since the Euclidean algorithm on positive integers $m,n$ terminates in finitely many steps (the remainders form a strictly decreasing sequence of nonnegative integers), the algorithm on $a,b$ also terminates—yielding as last nonzero remainder $(\gcd(m,n))d$, a common measure.

Therefore the Euclidean algorithm halts on $a,b$ if and only if $a$ and $b$ are commensurable. ∎

Thursday, July 31, 2025

A monad is just …

There’s something of an internet meme about repeating the exact definition “a monad is just a monoid in the category of endofunctors” (sometimes “just” is emphasized to highlight how simple it is). I’m not fully sure on the meme history, but I believe it is something like, these programming kids get into programming languages that have a “monad” construct, and they’ve never seen this in their other languages (which just have normal stuff like strings, ints, arrays, etc) so they ask what a monad is, and then suddenly they get this stuff from algebra (monoid is from algebra I think?) and category theory which they haven’t studied because, like, not exactly a prerequisite for being a programmer. And the people trying to explain it can’t think of any clearer way to put it, there’s just no better way to explain it (as far as the people who understand it know) so whenever the kid goes to ask someone else they always get the same cryptic definition. That sound right? Anyway, uh, what does the definition mean anyway?

Yeah, your “meme origin” sketch is basically right. In the Haskell / functional programming world (and more broadly among people who’ve seen the category-theoretic formulation), the phrase

“A monad is just a monoid in the category of endofunctors

(or with the emphasized “just” as if to say “it’s not some mystical thing, it’s this one neat abstract fact”) gets repeated because it’s compact and elegant if you already know what all the words mean. To someone who hasn’t seen category theory, it sounds like being told “a car is just a four-wheeled combustion-powered device with a steering wheel in the category of road vehicles.” It’s accurate, but not immediately illuminating. So beginners ask “what’s a monad?” and get handed this line, which leads to the meme of the cryptic “insider answer” being recycled, often without the unpacking that makes it usable.

So: what does it mean?

Break it down:

1. Monoid (familiar algebraic idea)

A monoid is something with:

  • A binary operation that’s associative: (a ⋆ b) ⋆ c = a ⋆ (b ⋆ c).
  • An identity element e such that e ⋆ x = x = x ⋆ e.

Example: strings under concatenation. Operation is ++, identity is the empty string.

2. Category

A category consists of:

  • Objects (in programming, types like Int, Bool, etc.).
  • Morphisms between objects (in programming, functions between types).
  • Composition of morphisms that’s associative, and each object has an identity morphism.

The category most functional programmers implicitly work in is something like Hask: types as objects, pure functions as morphisms.

3. Functor and Endofunctor

A functor is a mapping between categories that preserves structure: it maps objects to objects and morphisms to morphisms in a way that respects composition and identities.

An endofunctor is a functor from a category to itself. In Hask, examples are type constructors like:

  • Maybe (which adds “possible failure” context),
  • [] (lists, i.e., nondeterminism or multiple results),
  • IO (side effects),
  • State s (stateful computations), etc.

Each of these takes a type a and yields a new type (e.g., Maybe a), and you can map a function over their contents (fmap).

4. Category of endofunctors

Now consider all endofunctors on Hask as objects in a new category. The morphisms between those are natural transformations (uniform ways of going from one functor to another). This whole setup itself is a category: the category of endofunctors.

This category has a built-in notion of “combining” endofunctors: functor composition. Given two endofunctors M and M again, you can compose them to get M ∘ M (e.g., Maybe (Maybe a)).

5. Monoid in that category

A monoid object in this category (with composition as the “multiplication”) is:

  • An endofunctor M,
  • A natural transformation η: Id ⇒ M (the identity functor injecting into M), called the unit,
  • A natural transformation μ: M ∘ M ⇒ M (flattening two layers of M into one), called the multiplication,

such that the usual monoid laws hold, but now in the setting of natural transformations and functor composition:

  • Associativity: flattening nested Ms in either grouping gives the same result (μ ∘ Mμ = μ ∘ μM),
  • Unit laws: injecting then flattening, or flattening then injecting, is a no-op (μ ∘ Mη = id = μ ∘ ηM).

6. That’s a monad

That (M, η, μ) satisfying those laws is what category theory calls a monad. So a monad is literally a monoid in the category whose objects are endofunctors and whose “multiplication” is composition. Hence the pithy definition.

7. Programming translation

In programming (e.g., Haskell) people usually work with:

  • returnη (puts a pure value into the monadic context),
  • joinμ (flattens M (M a) into M a),
  • bind (>>=) is a derived helper that sequences: given m :: M a and f :: a -> M b, m >>= f is join (fmap f m).

The monad laws in the familiar form:

  • Left identity: return a >>= f ≡ f a
  • Right identity: m >>= return ≡ m
  • Associativity: (m >>= f) >>= g ≡ m >>= (\x -> f x >>= g)

These correspond exactly to the monoid laws on η and μ.

8. Intuition (why people often give other explanations)

Saying “a monad is a way to sequence computations that carry along some context” gives operational intuition: the context might be “possible failure” (Maybe), “multiple results” (List), “state threading” (State), etc. The abstract definition packages all such patterns uniformly: you get a “contextual computation” with a way to inject pure values and a way to compose/flatten nested contexts, obeying clean laws.

9. Example: Maybe

  • η x = Just x (inject a value),
  • μ (Just (Just x)) = Just x, μ (Just Nothing) = Nothing, μ Nothing = Nothing (flatten nested Maybes),
  • bind handles failure automatically.

This satisfies the associativity and identity laws, so Maybe is a monad.


So the meme is that the answer “a monad is just a monoid in the category of endofunctors” is technically satisfying and compact, but for learners it’s helpful to unpack it in terms of return/bind/join and concrete examples. Once you’ve internalized the structure, the categorical definition becomes a crisp way of seeing why all those disparate-looking patterns (IO, lists, exceptions, state) share the same abstract shape.

If you want, I can give several concrete examples side by side, derive bind from μ/fmap, or draw the commutative diagrams that encode the laws. Which angle would help most next?

This post is probably not as good as the StackOverflow answer.

Tuesday, July 29, 2025

Vector spaces under multiplication (o4-mini-high)

What are vector spaces under multiplication?

In the usual definition, a vector space $V$ over a field $K$ consists of

  1. An abelian group $(V,+)$ (the “vector-addition”), with identity element $0$.

  2. A scalar multiplication $K\times V\to V$, satisfying

    • $1\cdot v = v$,
    • $(\alpha\beta)\cdot v = \alpha\cdot(\beta\cdot v)$,
    • $\alpha\cdot(u+v) = \alpha\cdot u + \alpha\cdot v$,
    • $(\alpha+\beta)\cdot v = \alpha\cdot v + \beta\cdot v$.

1. “Multiplicative” notation for vector spaces

Nothing deep changes if you write your abelian group multiplicatively instead of additively. Concretely, you replace

  • the additive identity $0$ by a multiplicative identity, call it $1_V$,
  • the “sum” $u+v$ by a product $u\cdot v$,
  • the inverse $-v$ by the group-inverse $v^{-1}$.

Then the scalar axioms become:

  1. Unit:

    $$ 1_K \cdot v = v . $$

  2. Compatibility of scalars:

    $$ (\alpha\beta)\cdot v \;=\; \alpha\cdot(\beta\cdot v). $$

  3. Distributes over the group product:

    $$ \alpha\cdot(u\cdot v) \;=\; (\alpha\cdot u)\,\cdot\,(\alpha\cdot v). $$

  4. Respects field-addition via the group product:

    $$ (\alpha+\beta)\cdot v \;=\; (\alpha\cdot v)\,\cdot\,(\beta\cdot v). $$

Terminology. A set $V$ equipped with a (written-multiplicative) abelian group structure and a field-action satisfying the four rules above is sometimes called a multiplicative or exponential vector space over $K$. But it is completely equivalent (indeed isomorphic) to an ordinary (additive) vector space: you just rewrite $u\cdot v$ as “$u+v$” and $1_V$ as “0.”


1.1 Example: $\Bbb R_{>0}$

  • Take $V=\{\,x>0\}$ with multiplication as the group law.

  • For any real scalar $t$, define

    $$ t\;\cdot\;x \;=\; x^t. $$

    Then one checks:

    • $1\cdot x = x^1 = x$.
    • $(s+t)\cdot x = x^{\,s+t} = x^s \cdot x^t = (s\cdot x)\,(t\cdot x)$.
    • $s\cdot(xy) = (xy)^s = x^s y^s = (s\cdot x)\,(s\cdot y)$.
    • $(st)\cdot x = x^{\,st} = s\cdot(x^t) = s\cdot(t\cdot x)$.

Thus $\bigl(\mathbb{R}_{>0},\times\bigr)$ with exponentiation is a vector space over $\mathbb{R}$, written multiplicatively; under the isomorphism $x\mapsto\ln x$ it becomes the usual additive space $(\mathbb{R},+)$.


2. Vector spaces “with” multiplication of vectors ≠

Sometimes one asks: “What if I try to multiply two vectors to get another vector?” That additional operation (bilinear in the vectors) turns your space into an algebra rather than a “vector space under multiplication.” In other words:

  • A plain vector space only has

    1. an addition of vectors, and
    2. a scalar multiplication by field elements.
  • A (unital) algebra over $K$ is a vector space $V$ plus a map

    $$ V\times V \;\longrightarrow\; V,\quad (u,v)\mapsto u\,v, $$

    which is bilinear:

    $$ (\alpha u)\,v = \alpha\,(u\,v),\quad u\,(\alpha v)=\alpha\,(u\,v),\quad (u+u')\,v = u\,v + u'\,v,\quad\ldots $$

    Examples include:

    • The space of $n\times n$ matrices with ordinary matrix‐multiplication,
    • Polynomial rings $K[x]$ with polynomial multiplication,
    • Any group algebra, Clifford algebra, etc.

Summary

  • If by “vector spaces under multiplication” you mean writing the abelian group law multiplicatively, you get the same notion of vector space, with scalar action behaving like exponentiation (e.g.\ $\mathbb{R}_{>0}$ with $x^t$).
  • If you mean equipping a vector space with a new “vector–vector” product, you’re describing an algebra over the field, which is a strictly richer structure.

Either argument establishes that the set of all finite sequences of natural numbers is countable.

Note: ChatGPT’s uses of \R, which rendered correctly in its own UI, were changed into \mathbb{R} here.

Thursday, July 24, 2025

Rotation, reflection, orientation

Is this true:
“Suppose that a line is chosen from within a plane, and the plane is rotated 180° clockwise around this line. This will mirror the plane, reflecting its points such that the points leftward from the line become rightward from it and vice-versa. But suppose that the rotation is done counter-clockwise instead. Then the same thing happens, and there is no difference.”

Yes – it’s absolutely true. Here’s why:

  1. A 180° rotation about a line in the plane acts like a mirror reflection across that line.

    • Take for example the horizontal $x$–axis in the $xy$–plane. If you rotate the plane 180° about that axis (in three‑dimensional space), any point $(x,y,0)$ goes to $(x,-y,0)$. That is exactly the effect of reflecting across the $x$–axis.
    • More generally, for any line $L\subset P$, the restriction to $P$ of the half‐turn about $L$ is the involution “reflect across $L$.”
  2. “Clockwise” versus “counter‑clockwise” makes no difference at 180°.

    • Angles are taken modulo 360°, so rotating by $+180°$ or by $-180°$ lands you in the same place:

    $$ R_{L}(+180°)\;=\;R_{L}(-180°). $$

    • In other words, a half‑turn is its own inverse, and the sign convention for direction drops out.
  3. Orientation flip on the plane.

    • In three dimensions the rotation by 180° about a line lying in a plane reverses the plane’s “sidedness” (it sends a chosen normal vector to its negative). That’s exactly what a reflection in the line does: it reverses orientation in the plane.

So rotating $P$ by 180° around $L$ is the same map as reflecting $P$ across $L$, and whether you spin “clockwise” or “counter‑clockwise” your half‑turn is identical.

Wednesday, July 23, 2025

Set of finite sequences of naturals: countable

Show that the set $${\mathbb N}^{<{\mathbb N}} = \cup _{k=0}^{\infty } {\mathbb N}^{\{0, 1, \ldots , k-1\}}$$ of all finite sequences of natural numbers is countable.

Here are two standard ways to see that the set of all finite sequences of naturals

$$ \mathbb{N}^{<\mathbb{N}} \;=\;\bigcup_{k=0}^\infty \mathbb{N}^{\{0,1,\dots,k-1\}} $$

is countable.


1. Union of countably many countable sets

  1. Each fixed‑length piece is countable. For each $k\ge1$, the set of $k$‑tuples

    $$ \mathbb{N}^k = \{\, (n_0,n_1,\dots,n_{k-1}) : n_i\in\mathbb{N}\} $$

    is in bijection with $\mathbb{N}$. For example, one can use the usual Cantor–Gödel pairing repeatedly to pair off coordinates:

    $$ \pi:\mathbb{N}\times\mathbb{N}\;\xrightarrow{\sim}\;\mathbb{N}, \quad (a,b)\;\mapsto\;\tfrac12(a+b)(a+b+1)+b, $$

    and then pair $\pi(\pi(n_0,n_1),n_2)$, etc., to get a bijection $\mathbb{N}^k\cong\mathbb{N}$.

  2. A countable union of countable sets is countable. We have

    $$ \mathbb{N}^{<\mathbb{N}} \;=\; \bigcup_{k=0}^\infty \mathbb{N}^{k} \,, $$

    and since each $\mathbb{N}^k$ is countable, their (countable) union is again countable.

Hence $\mathbb{N}^{<\mathbb{N}}$ is countable.


2. Explicit injective coding via prime factorisation

Define a map

$$ \Phi:\mathbb{N}^{<\mathbb{N}}\;\longrightarrow\;\mathbb{N}_{>0} $$

by sending a finite sequence

$$ s=(n_0,n_1,\dots,n_{k-1}) $$

to the integer

$$ \Phi(s) \;=\; 2^{n_0}\,\cdot\,3^{n_1}\,\cdot\,5^{n_2}\,\cdots\,p_k^{\,n_{k-1}} \quad \bigl(p_k\text{ = the }k\text{th prime}\bigr). $$

  • Injectivity. By the Fundamental Theorem of Arithmetic, every positive integer has a unique prime‑power factorisation; in particular, different sequences $s$ give different factorizations, so $\Phi$ is injective.
  • Conclusion. An injective map from $\mathbb{N}^{<\mathbb{N}}$ into $\mathbb{N}$ shows $\#\mathbb{N}^{<\mathbb{N}}\le\#\mathbb{N}$. Since $\mathbb{N}^{<\mathbb{N}}$ is infinite, its cardinality is exactly $\aleph_0$.

Either argument establishes that the set of all finite sequences of natural numbers is countable.

Note: ChatGPT’s uses of \N, which rendered correctly in its own UI, were changed into \mathbb{N} here.

Modular arithmetic

These first questions were all with o4-mini. The theorem statements are all from this book. I found ChatGPT’s proofs more didactic than the originals.

For any fixed natural number m greater than 1, we say that the integer a is congruent to the integer b modulo m if a − b is divisible by m. Show that the congruence relation (modulo m) is transitive.

To prove transitivity, start with the hypotheses:

  1. $a \equiv b \pmod m$. This means $m$ divides $a-b$, so there exists an integer $k$ such that

    $$ a - b = km. $$

  2. $b \equiv c \pmod m$. This means $m$ divides $b - c$, so there exists an integer $ℓ$ such that

    $$ b - c = ℓm. $$

Now consider $a - c$. We can write

$$ a - c \;=\; (a - b) + (b - c) \;=\; km + ℓm \;=\; (k + ℓ)\,m. $$

Since $k + ℓ$ is an integer, this shows $m$ divides $a-c$. Hence

$$ a \equiv c \pmod m, $$

which is exactly the statement of transitivity for congruence modulo $m$.

Show that when a and b are nonnegative integers, the relationship a ≡ b (mod m) is equivalent to a and b leaving equal remainders upon division by m.

To show the two statements are equivalent, we prove each implication in turn.


1. If $a \equiv b \pmod m$, then $a$ and $b$ have the same remainder upon division by $m$.

Write the Euclidean divisions of $a$ and $b$ by $m$:

$$ \begin{aligned} a &= q_a\,m + r_a, \quad 0 \le r_a < m,\\ b &= q_b\,m + r_b, \quad 0 \le r_b < m. \end{aligned} $$

Then

$$ a - b = (q_a - q_b)\,m + (r_a - r_b). $$

If $a \equiv b \pmod m$, by definition $m$ divides $a - b$. But

$$ a - b = (q_a - q_b)\,m + (r_a - r_b) $$

and $(q_a - q_b)\,m$ is already a multiple of $m$. For the whole sum to be a multiple of $m$, the “leftover” term $r_a - r_b$ must also be a multiple of $m$. Since

$$ |r_a - r_b| < m, $$

the only multiple of $m$ in that range is $0$. Hence

$$ r_a - r_b = 0 \quad\Longrightarrow\quad r_a = r_b. $$


2. If $a$ and $b$ leave the same remainder upon division by $m$, then $a \equiv b \pmod m$.

Again write

$$ a = q_a\,m + r,\quad b = q_b\,m + r \quad\text{with }0 \le r < m. $$

Then

$$ a - b = (q_a\,m + r) - (q_b\,m + r) = (q_a - q_b)\,m, $$

which is manifestly divisible by $m$. Therefore $a \equiv b \pmod m$.


Combining (1) and (2) shows the two characterizations are logically equivalent.

Show that for a given modulus m, each integer is congruent to exactly one of the numbers in the set {0, 1, 2, …, m − 1}.

Proof. Fix an integer $m>1$. We must show that for any integer $a$ there is one—and only one—remainder $r$ with

$$ 0 \le r \le m-1 $$

such that

$$ a \equiv r \pmod m. $$


Existence

By the Division Algorithm, for any integer $a$ there exist unique integers $q$ and $r$ with

$$ a = q\,m + r \quad\text{and}\quad 0 \,\le\, r \,<\, m. $$

Rewriting gives

$$ a - r = q\,m, $$

so $m$ divides $a-r$, i.e.\ $a\equiv r\pmod m$. Thus some $r$ in $\{0,1,\dots,m-1\}$ is congruent to $a$.


Uniqueness

Suppose $r$ and $s$ are two remainders in $\{0,1,\dots,m-1\}$ with

$$ a \equiv r \pmod m \quad\text{and}\quad a \equiv s \pmod m. $$

Then $m$ divides both $a-r$ and $a-s$, so it also divides their difference:

$$ m \mid \bigl[(a-r) - (a-s)\bigr] \;\Longrightarrow\; m \mid (s - r). $$

But $r$ and $s$ lie in the range $0\le r,s< m$, so $|s-r|<m$. The only multiple of $m$ in that range is $0$. Hence

$$ s - r = 0 \quad\Longrightarrow\quad s = r. $$


Combining these two parts shows each integer $a$ is congruent to exactly one element of $\{0,1,2,\dots,m-1\}$.

Show that if a ≡ b (mod m) and c ≡ d (mod m), then (i) (a + c) ≡ (b + d) (mod m), and
(ii) ac ≡ bd (mod m).

Proof. Let $m>1$ be fixed, and suppose

$$ a \equiv b \pmod m \quad\text{and}\quad c \equiv d \pmod m. $$

By definition, there exist integers $k$ and $\ell$ such that

$$ a - b = k\,m, \qquad c - d = \ell\,m. $$


(i) Addition

Consider

$$ (a + c) \;-\;(b + d) = (a - b) + (c - d) = k\,m + \ell\,m = (k + \ell)\,m. $$

Since $(k+\ell)$ is an integer, $m$ divides $(a+c)-(b+d)$. Hence

$$ a + c \equiv b + d \pmod m. $$


(ii) Multiplication

Consider

$$ a\,c \;-\; b\,d = a\,c \;-\; b\,c \;+\; b\,c \;-\; b\,d = c\,(a - b) \;+\; b\,(c - d). $$

Substituting the expressions above,

$$ c\,(a - b) + b\,(c - d) = c\,(k\,m) + b\,(\ell\,m) = (c\,k + b\,\ell)\,m, $$

which shows $m$ divides $ac - bd$. Therefore

$$ ac \equiv bd \pmod m. $$


Thus congruence modulo $m$ is compatible with both addition and multiplication.

Show that if a ≡ b (mod m), then a^n ≡ b^n (mod m), for every natural number n.

Proof by induction on $n$.

Fix a modulus $m>1$ and suppose $a \equiv b \pmod m$. We show $a^n \equiv b^n\pmod m$ for every natural number $n$.


Base case ($n=1$)

Trivially,

$$ a^1 = a \equiv b = b^1 \pmod m. $$


Inductive step

Assume for some $k\ge1$ that

$$ a^k \equiv b^k \pmod m. $$

We must show

$$ a^{k+1} \equiv b^{k+1} \pmod m. $$

Notice

$$ a^{k+1} \;=\; a^k \cdot a, \qquad b^{k+1} \;=\; b^k \cdot b. $$

By the inductive hypothesis $a^k \equiv b^k\pmod m$, and by our original assumption $a\equiv b\pmod m$. From the compatibility of congruence with multiplication (proved earlier), we conclude

$$ a^{k+1} = a^k\cdot a \;\equiv\; b^k\cdot b = b^{k+1} \pmod m. $$


By induction, $a^n \equiv b^n \pmod m$ for all natural numbers $n$. This completes the proof.

Show that every natural number is congruent to the sum of its digits modulo 9.

Proof. Every natural number $N$ can be written in its decimal expansion as

$$ N = d_0 + d_1\cdot 10 + d_2\cdot 10^2 + \cdots + d_k\cdot 10^k, $$

where each $d_i$ is one of the digits $0,1,\dots,9$. Call

$$ S = d_0 + d_1 + d_2 + \cdots + d_k $$

the sum of its digits.

We will show

$$ N \equiv S \pmod 9. $$


  1. Observe that $10 \equiv 1 \pmod 9$. Indeed, $10-1=9$ is divisible by 9.

  2. Use the fact that congruence is compatible with powers and multiplication. From $10\equiv1\pmod9$ it follows by induction (or by repeatedly applying the multiplicative property) that

    $$ 10^i \equiv 1^i = 1 \pmod 9 \quad\text{for every }i\ge0. $$

  3. Apply congruence to each term in the expansion of $N$.

    $$ N = \sum_{i=0}^k d_i\,10^i \equiv \sum_{i=0}^k d_i\cdot1 = \sum_{i=0}^k d_i = S \pmod 9. $$

Since every step uses only the rules of congruence (namely that if $x\equiv y$ and $u\equiv v\pmod m$, then $xu\equiv yv\pmod m$ and sums likewise agree), we conclude

$$ N \equiv S \pmod 9, $$

i.e.\ every natural number is congruent modulo 9 to the sum of its digits.

For this question I used o4-mini-high.

Summarize and contextualize the knowledge about congruences we have seen so far.

Here’s a bird’s‑eye view of what we’ve built up so far in our study of congruences modulo $m$, and why it matters:


1. Congruence as an Equivalence Relation

  • Definition:

    $$ a \equiv b \pmod m \quad\Longleftrightarrow\quad m \mid (a - b). $$

  • Properties:

    • Reflexive: $a\equiv a\pmod m$.
    • Symmetric: If $a\equiv b$, then $b\equiv a$.
    • Transitive: If $a\equiv b$ and $b\equiv c$, then $a\equiv c$.

Together, these show congruence partitions the integers into equivalence classes.


2. Equivalence Classes and Canonical Representatives

  • Every integer $a$ is congruent to exactly one of

    $$ \{0,1,2,\dots,m-1\}, $$

    its remainder upon division by $m$.

  • Thus the set of all integers splits into $m$ classes, often denoted $\Bbb Z/m\Bbb Z = \{[0],[1],\dots,[m-1]\}$.


3. Arithmetic in $\Bbb Z/m\Bbb Z$

Congruence “respects” the usual operations: if

$$ a\equiv b\pmod m, \quad c\equiv d\pmod m, $$

then

  1. Addition: $(a+c)\equiv(b+d)\pmod m.$
  2. Multiplication: $a\,c\equiv b\,d\pmod m.$
  3. Powers: $a^n\equiv b^n\pmod m$ for all $n\in\Bbb N$.

Because of this, one can do arithmetic “mod $m$” and get a well‑defined algebraic system (a ring).


4. A Classic Application: The “Divisibility by 9” Test

  • Writing a decimal number $N$ as $\sum d_i\,10^i$, and using $10\equiv1\pmod9$, shows

    $$ N \equiv \sum d_i \pmod9. $$

  • Hence a number is divisible by 9 exactly when the sum of its digits is.


5. Why This Matters

  • Number‑theoretic tools: solving linear congruences, understanding invertibility (“units” in $\Bbb Z/m\Bbb Z$), working toward Fermat’s Little Theorem and Euler’s Theorem.
  • Chinese Remainder Theorem: decomposing problems mod $m$ into simpler ones mod its prime‑power factors.
  • Cryptography & coding theory: RSA, Diffie–Hellman, checksums all rely on modular arithmetic.
  • Practical divisibility tests: beyond 9, there are tests for 3, 11, 7, etc., all built on the same congruence ideas.

In short, congruences give us a way to “collapse” the infinite set of integers into finitely many residue classes while still doing meaningful arithmetic—and that simple idea underpins huge swaths of both pure and applied mathematics.

Monday, July 21, 2025

Division Algorithm for Polynomials

Prove this: Let f(x), g(x) be two polynomials with deg(f) ≤ deg(g). Then there are unique polynomials q(x), r(x) such that g(x) = q(x)f(x) + r(x), where deg(r) < deg(f).

Proof (Division Algorithm for Polynomials)

Let $f(x)$ and $g(x)$ be polynomials over a field (or integral domain) $K$, with

$$ \deg(f) \le \deg(g). $$

We will show:

  1. Existence: there exist polynomials $q(x)$ and $r(x)$ such that

    $$ g(x) = q(x)\,f(x) + r(x), $$

    with $\deg(r)<\deg(f)$.

  2. Uniqueness: this representation is unique.


1. Existence

We proceed by induction on $n = \deg(g)$.

  • Base case: If $\deg(g) < \deg(f)$, then we may simply take

    $$ q(x) = 0, \qquad r(x) = g(x). $$

    Clearly $g = 0\cdot f + g$, and $\deg(r)=\deg(g)<\deg(f)$.

  • Inductive step: Suppose the statement holds for all polynomials of degree less than $n$, and let $\deg(g)=n\ge\deg(f)$. Write

    $$ f(x) = a_m x^m + \dots,\quad g(x)=b_n x^n + \dots $$

    with $a_m,b_n\neq0$. Since $n\ge m$, set

    $$ t(x) \;=\;\frac{b_n}{a_m}\,x^{n-m}. $$

    Then $\deg\bigl(t(x)\,f(x)\bigr)=n$, and its leading coefficient matches that of $g(x)$. Consider

    $$ g_1(x) \;=\; g(x)\;-\;t(x)\,f(x). $$

    By construction, $\deg(g_1)<n$. Now by the induction hypothesis applied to $g_1$ (whose degree is $<n$), there exist polynomials $q_1(x)$ and $r(x)$ with

    $$ g_1(x) = q_1(x)\,f(x) + r(x), \qquad \deg(r)<\deg(f). $$

    Hence

    $$ g(x) = t(x)\,f(x) + g_1(x) = t(x)\,f(x) + \bigl(q_1(x)\,f(x) + r(x)\bigr) = \bigl(t(x)+q_1(x)\bigr)\,f(x) + r(x). $$

    Setting $q(x)=t(x)+q_1(x)$ gives the desired representation for $g$, with $\deg(r)<\deg(f)$.

By induction, such $q(x)$ and $r(x)$ always exist.


2. Uniqueness

Suppose there are two representations

$$ g(x) = q_1(x)\,f(x) + r_1(x), \qquad g(x) = q_2(x)\,f(x) + r_2(x), $$

with $\deg(r_1),\deg(r_2)<\deg(f)$. Subtracting gives

$$ 0 = \bigl(q_1(x)-q_2(x)\bigr)\,f(x)\;+\;\bigl(r_1(x)-r_2(x)\bigr). $$

Rearrange:

$$ \bigl(q_1(x)-q_2(x)\bigr)\,f(x) = -\bigl(r_1(x)-r_2(x)\bigr). $$

  • The left side is a multiple of $f(x)$, so if $q_1\neq q_2$ then $\deg\bigl((q_1-q_2)f\bigr)\ge \deg(f)$.
  • The right side is $r_2(x)-r_1(x)$, whose degree is strictly less than $\deg(f)$.

The only way a nonzero polynomial of degree $\ge\deg(f)$ can equal one of degree $<\deg(f)$ is if both are the zero polynomial. Hence

$$ q_1(x)-q_2(x) \;=\; 0, \quad r_1(x)-r_2(x)\;=\;0, $$

so $q_1 = q_2$ and $r_1 = r_2$. This proves uniqueness.


Together, these two parts establish the Division Algorithm for polynomials: for any $f(x)$ and $g(x)$ with $\deg(f)\le\deg(g)$, there exist unique polynomials $q(x)$ and $r(x)$ such that

$$ g(x) = q(x)\,f(x) + r(x), \quad \deg(r)<\deg(f). $$

$\quad\blacksquare$

Friday, July 11, 2025

Proof of Binet’s formula

Where x_n is the nth Fibonacci number, prove that $ \forall n \in \mathbb{N}, x_n = \frac {1}{\sqrt{5}} \left[\left(\frac {1 + \sqrt {5}}{2}\right)^n - \left(\frac {1 - \sqrt {5}}{2}\right)^n\right] $.

To prove the closed‐form (“Binet’s”) formula

$$ x_n \;=\;\frac1{\sqrt5}\Bigl(\phi^n - \psi^n\Bigr), \quad\text{where}\quad \phi = \frac{1 + \sqrt5}2,\quad \psi = \frac{1 - \sqrt5}2, $$

we’ll show that the right‐hand side satisfies

  1. the same recurrence as the Fibonacci numbers,
  2. the same initial conditions,

and then invoke uniqueness of solutions to a linear recurrence.


1. Setup: characteristic equation

The Fibonacci numbers are defined by

$$ x_0 = 0,\quad x_1 = 1,\quad x_n = x_{n-1} + x_{n-2}\quad(\forall n\ge2). $$

The associated characteristic polynomial is

$$ r^2 - r - 1 = 0, $$

whose two roots are

$$ \phi = \tfrac{1+\sqrt5}2, \qquad \psi = \tfrac{1-\sqrt5}2. $$

Since this is a distinct‐root second‐order homogeneous recurrence, its general solution is

$$ a_n \;=\; A\,\phi^n + B\,\psi^n $$

for constants $A,B$ determined by $a_0$ and $a_1$.


2. Determine $A$ and $B$ via initial conditions

We want $a_n = x_n$, so impose

  1. $a_0 = A + B = x_0 = 0$,
  2. $a_1 = A\,\phi + B\,\psi = x_1 = 1$.

From $A+B=0$ we get $B=-A$. Plugging into the second,

$$ A\,\phi - A\,\psi \;=\; 1 \quad\Longrightarrow\quad A(\phi - \psi) = 1. $$

But $\phi - \psi = \sqrt5$, so $A = 1/\sqrt5$ and hence $B = -1/\sqrt5$. Therefore

$$ a_n \;=\;\frac1{\sqrt5}\,\phi^n \;-\;\frac1{\sqrt5}\,\psi^n \;=\;\frac1{\sqrt5}\bigl(\phi^n - \psi^n\bigr). $$


3. Check that $a_n$ satisfies the Fibonacci recurrence

Since $\phi$ and $\psi$ are roots of $r^2 = r+1$, we have

$$ \phi^n = \phi^{n-1} + \phi^{n-2}, \quad \psi^n = \psi^{n-1} + \psi^{n-2}. $$

Hence

$$ a_n =\frac{\phi^n - \psi^n}{\sqrt5} =\frac{(\phi^{n-1}+\phi^{n-2}) - (\psi^{n-1}+\psi^{n-2})}{\sqrt5} =\frac{\phi^{n-1}-\psi^{n-1}}{\sqrt5} \;+\;\frac{\phi^{n-2}-\psi^{n-2}}{\sqrt5} =a_{n-1} + a_{n-2}. $$

Thus $a_n$ obeys the same recurrence as $x_n$.


4. Conclusion by uniqueness

We have produced a sequence $a_n=(\phi^n-\psi^n)/\sqrt5$ which

  • satisfies the same recurrence $a_n = a_{n-1}+a_{n-2}$,
  • has the same initial values $a_0=0$, $a_1=1$.

By the standard uniqueness theorem for linear recurrences, it follows that

$$ a_n = x_n \quad\forall n\in\Bbb N. $$

That is,

$$ \boxed{ x_n =\frac1{\sqrt5}\Bigl(\phi^n - \psi^n\Bigr) =\frac1{\sqrt5}\Bigl(\bigl(\tfrac{1+\sqrt5}2\bigr)^n -\bigl(\tfrac{1-\sqrt5}2\bigr)^n\Bigr). } $$

This completes the proof. ∎

Proof player 2 always wins by mirroring (Nim)

One version of a game called Nim is played as follows. There are two players and two piles consisting of the same natural number of objects; for this example, suppose the objects are nickels. At each turn, a player removes some number of nickels from either one of the piles. Then the other player removes some number of nickels from either of the piles. The players continue playing alternately until the last nickel is removed. The winner is the player who removes the last nickel.

Prove: If the second player always removes the same number of nickels that the first player last removed and does so from the other pile (thus making the piles equal in number after the second player’s turn), then the second player will win.

Here’s a clean “mirror”‐strategy proof that the second player (P₂) will always win.


Setup. – Two piles, each of size $n$. – Players alternate: P₁ goes first, then P₂, etc. – On your turn you remove any positive number of nickels from exactly one pile. – Whoever takes the very last nickel (i.e. empties the last nonempty pile) wins.


1. The Mirror Strategy

P₂’s rule. On each of P₁’s moves, say P₁ removes $k$ nickels from one of the piles. Then P₂ immediately removes $k$ nickels from the other pile.

Because the two piles were equal just before P₁’s move, removing $k$ from one leaves one pile of size $n-k$ and the other still size $n$. Then P₂ can legally remove $k$ from that size-$n$ pile (since $k\le n$), restoring both piles to size $n-k$.

Thus:

Invariant. At the end of each of P₂’s turns, the two piles are the same size.


2. Why This Guarantees a Win

  1. Base case (start of play). Before anyone moves, both piles are size $n$. So the invariant holds.

  2. Preservation. If at the end of one of P₂’s turns both piles are size $m$, then:

    1. P₁ removes $k$ from one pile (with $1\le k\le m$), leaving piles $(m-k,m)$.
    2. P₂ then removes $k$ from the other pile, leaving $(m-k,m-k)$.
    3. Hence after P₂’s turn both piles are again the same size ($m-k$).
  3. Termination & win. Eventually the piles shrink down. The first time P₁ empties a pile, say he takes all $m$ remaining nickels from one pile, we have:

    $$ \text{After P₁’s move: piles } (0,\,m). $$

    By the invariant $m$ is exactly how many are in the “other” pile. P₂ then removes those last $m$ nickels from that other pile—legally and wholly—leaving $(0,0)$ and thus taking the very last coin.

Since P₂ never violates the rules (he always takes at most what’s available), and since this mirror‐move process ensures he makes the final removal, P₂ is guaranteed to win. ∎

Wednesday, May 28, 2025

Bertrand postulate proof sketch

Prove that ∀n∈ℕ(n>1⟹∃p∈ℙ(n<p<2n)).

Below is a classical proof of

$$ \forall n\in\Bbb N,\;n>1\;\Longrightarrow\;\exists\,p\in\Bbb P\text{ with }n<p<2n, $$

first conjectured by Bertrand in 1845 and proved (independently) by Chebyshev in 1852. It is often called Bertrand’s postulate.


1. Chebyshev’s θ-function

Define

$$ \theta(x)\;=\;\sum_{\substack{p\le x\\p\;\text{prime}}}\ln p. $$

Chebyshev showed by completely elementary means (bounding factorials via products) that there are absolute constants $A,B>0$ such that for all $x\ge2$,

$$ A\,x\;\le\;\theta(x)\;\le\;B\,x. $$

One may take for instance

$$ 0.92\,x\;\le\;\theta(x)\;\le\;1.11\,x, $$

for every $x\ge2$. (The proof is a straightforward—but somewhat lengthy—analysis of the prime‐power factors of $n!$.)


2. From θ to “a prime in $(n,2n)$”

Observe that

$$ \theta(2n)\;-\;\theta(n)\;=\;\sum_{\,n< p\le 2n}\ln p. $$

If there were no prime in the open interval $(n,2n]$, then $\theta(2n)-\theta(n)=0$. But by Chebyshev’s bounds,

$$ \theta(2n)-\theta(n)\;\ge\;0.92\,(2n)\;-\;1.11\,n \;=\;(1.84-1.11)\,n \;=\;0.73\,n \;>\;0 \quad(\forall n>1). $$

Hence

$$ \sum_{\,n<p\le 2n}\ln p>0, $$

so there must be at least one prime $p\in(n,2n]$. This completes the proof.∎


Historical remarks

  • Chebyshev’s original 1852 proof bounds factorials $\;n!\approx\sqrt{2\pi n}\,(n/e)^n$ from above and below elementarily, then translates those bounds into $\;A\,x\le\theta(x)\le B\,x$.
  • In 1932, Erdős found a shorter “combinatorial” proof using properties of the central binomial coefficient $\binom{2n}{n}$.
  • The statement is often referred to as Bertrand’s postulate, though it is now a theorem.