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

Thursday, August 7, 2025

Rings vs semirings

Rings vs semirings

Overview A semiring is an algebraic structure with two binary operations (usually called “addition” and “multiplication”) satisfying most of the familiar ring axioms except that additive inverses need not exist. A ring, by contrast, does require that every element have an additive inverse. Below is a side-by-side comparison of their defining properties, followed by examples and some remarks on when each arises in mathematics and computer science.


1. Definitions and Axioms

Property Semiring Ring
Underlying set $S$ $R$
Addition $(+,0)$ Commutative monoid:$\forall a,b,c\in S$,$a+(b+c)=(a+b)+c$,$a+b=b+a$,$0+a=a$. Abelian group:All semiring properties plus$\forall a\exists(-a)$ such that $a+(-a)=0$.
Multiplication $(\cdot,1)$ Monoid:$\forall a,b,c\in S$,$a\cdot(b\cdot c)=(a\cdot b)\cdot c$,often with identity $1$. Monoid:Same as semiring. Often required to have $1$.
Distributivity $\forall a,b,c$,$a\cdot(b+c)=a\cdot b + a\cdot c$,$(b+c)\cdot a = b\cdot a + c\cdot a$. Same as semiring.
Zero-absorption $\forall a,;0\cdot a = a\cdot 0 = 0$. Same as semiring.
Additive inverses Not required Required

Note on terminology:

  • Some authors require a multiplicative identity $1$ in both structures; others call a structure without $1$ a “hemiring” (for semirings without identity) or a “rng” (for rings without unity).
  • In this summary we assume both have $1$, unless noted otherwise.

2. Key Differences

  1. Additive inverses

    • Ring: Every element $a$ has an inverse $-a$ so that $a + (-a) = 0$.
    • Semiring: May lack inverses; subtraction need not be defined.
  2. Examples

    • Semiring but not Ring:

      • $\mathbb{N} = {0,1,2,\dots}$ with usual $+$ and $\times$.
      • The Boolean semiring ${0,1}$ with $+$ = OR, $\cdot$ = AND.
    • Ring:

      • $\mathbb{Z}$, $\mathbb{Q}$, $\mathbb{R}$, $\mathbb{C}$.
      • Matrix rings $M_n(\mathbb{R})$.
  3. Subtraction vs. cancellation

    • In rings you can “cancel” addition using inverses: from $a + x = a + y$ infer $x=y$.
    • In semirings, one often works with cancellative semirings to get a weak form of cancellation, but it’s not automatic.
  4. Applications

    • Semirings:

      • Automata theory & formal languages (e.g.\ shortest-path semirings).
      • Tropical geometry (min-plus semiring).
      • Quantitative program analysis (probabilities, costs).
    • Rings:

      • Core algebra and number theory.
      • Algebraic geometry, homological algebra.

3. Examples in More Detail

  1. Natural numbers $(\mathbb{N}, +, \times)$

    • Satisfies all semiring axioms: $0$ is additive identity, $1$ is multiplicative identity.
    • Fails ring axioms because there is no $n\in\mathbb{N}$ with $3 + n = 0$.
  2. Tropical semiring

    • Underlying set $\mathbb{R}\cup{\infty}$.
    • “Addition” is $\min$, “multiplication” is $+$.
    • Used for shortest-path computations (Dijkstra, Bellman–Ford).
  3. Boolean semiring ${0,1}$

    • $a+b = \max(a,b)$ (logical OR), $a\cdot b = \min(a,b)$ (logical AND).
    • Fundamental in logic, circuit design, and automata.
  4. Integer ring $(\mathbb{Z}, +, \times)$

    • Every integer has an additive inverse.
    • Ring of central importance in algebra and number theory.

4. When to Use Which?

  • Use a semiring when only nonnegative (or otherwise “one-sided”) quantities make sense and subtraction would be inappropriate or undefined.
  • Use a ring when a full-fledged additive group structure (including negatives) is needed for constructions like modules, ideals, and homological tools.

Further Reading

  • Golan, J. S. Semirings and their Applications (Kluwer, 1999)
  • Dummit & Foote, Abstract Algebra (especially the sections on rings and modules)

By focusing on the presence or absence of additive inverses, you can quickly tell whether an algebraic system is a semiring or a ring—and choose the right framework for your application.

Linear equation systems have 0 ∨ 1 ∨ ∞ solutions

Show that a system of linear equations either has no solution, or exactly one solution, or infinitely many solutions.

Let $A\in\mathbb{R}^{m\times n}$, $b\in\mathbb{R}^m$, and consider the linear system

$$ Ax=b. $$

Let $S=\{x\in\mathbb{R}^n:Ax=b\}$ be its solution set. There are three possibilities:


1. No solution. The system is inconsistent (e.g., row-reduction of the augmented matrix $[A\mid b]$ produces a row $[0\ \cdots\ 0\mid c]$ with $c\neq 0$). Then $S=\varnothing$.


2. At least one solution. Suppose $x_0$ is a solution: $Ax_0=b$. Define the homogeneous solution space

$$ H=\{h\in\mathbb{R}^n:Ah=0\} $$

(which is a subspace of $\mathbb{R}^n$). Then for any $h\in H$, $A(x_0+h)=Ax_0+Ah=b+0=b$, so $x_0+H\subseteq S$. Conversely, if $x\in S$, then $A(x-x_0)=Ax-Ax_0=b-b=0$, so $x-x_0\in H$ and $x=x_0+h$ for some $h\in H$. Hence

$$ S = x_0 + H, $$

i.e., the solution set is an affine subspace parallel to the homogeneous solution space.

Now:

  • If $H=\{0\}$, then $S=\{x_0\}$: exactly one solution.
  • If $H$ contains a nonzero vector $h\ne 0$, then for each real scalar $t$, $x_0+th\in S$. If $t_1\ne t_2$, then $(x_0+t_1h)-(x_0+t_2h)=(t_1-t_2)h\ne 0$, so these are all distinct. Thus there are infinitely many solutions.

Conclusion: Over $\mathbb{R}$ (or any infinite field), a linear system has either no solution, exactly one solution, or infinitely many solutions—no other cardinalities occur.

Example:

  • $x+y=1,\ x+y=2$ is inconsistent ⇒ no solution.
  • $x+y=1,\ x-y=0$ has a unique solution $x=y=\tfrac12$.
  • $x+y=2,\ 2x+2y=4$ is consistent but dependent: $S=\{(t,2-t):t\in\mathbb{R}\}$ ⇒ infinitely many solutions.

(Note: over a finite field, the “infinitely many” case becomes “more than one” but still finite; the trichotomy above assumes the underlying field is infinite, as in $\mathbb{R}$.)

Quasigroups

Explain quasigroups

A quasigroup is an algebraic structure $(Q, *)$ consisting of a set $Q$ with a binary operation $*$ such that for every $a,b\in Q$ the equations

  • $a * x = b$, and
  • $y * a = b$

have unique solutions $x,y\in Q$. In other words, “division” is always possible and yields a single result on both sides. This is equivalent to saying that the Cayley (multiplication) table of a finite quasigroup is a Latin square: each element appears exactly once in each row and each column.

You can define two division operations:

  • Left division: $a\backslash b$ is the unique $x$ with $a * x = b$.
  • Right division: $b / a$ is the unique $y$ with $y * a = b$.

These satisfy identities like

$$ a * (a\backslash b) = b,\quad (b / a) * a = b,\quad a\backslash(a * b) = b,\quad (b * a)/a = b. $$

Basic properties:

  • Cancellation: From uniqueness of solutions, left and right cancellation hold: if $a * b = a * c$, then $b=c$; similarly $b * a = c * a$ implies $b=c$.
  • Not necessarily associative: The operation need not satisfy $(a * b) * c = a * (b * c)$.
  • No need for identity: A quasigroup may or may not have an identity element.

A loop is a quasigroup that does have a two-sided identity element $e$ such that $e * a = a = a * e$ for all $a$. So every group is a loop (and hence a quasigroup) with associativity; but loops in general need not be associative.

Examples:

  1. Group example (associative loop): $(\mathbb{Z},+)$ with addition is a quasigroup (in fact a group) because for any $a,b$ the equations $a + x = b$ and $y + a = b$ have unique solutions $x = b - a$, $y = b - a$. Identity $0$ exists and associativity holds.

  2. Non-associative quasigroup (no identity): Take $Q = \mathbb{Z}_3 = \{0,1,2\}$ and define $a * b = a - b \pmod 3$. Then for any $a,b$, $a * x = b$ has unique solution $x = a - b$, and similarly on the left. There is no single element acting as a two-sided identity for all $a$, and the operation is not associative in general. So $(\mathbb{Z}_3, a * b = a - b)$ is a quasigroup but not a loop or group.

  3. Latin square example: Let $Q=\{1,2,3\}$ with operation given by the Latin square

$$ \begin{array}{c|ccc} * & 1 & 2 & 3\\\hline 1 & 2 & 3 & 1\\ 2 & 3 & 1 & 2\\ 3 & 1 & 2 & 3 \end{array} $$

This defines a quasigroup structure (every element appears once per row/column). It’s nontrivial to check associativity; this particular one is not necessarily associative, so it’s a non-associative quasigroup.

Special notions:

  • Isotopy: Two quasigroups $(Q,*)$ and $(Q,\circ)$ are isotopic if there exist bijections $\alpha,\beta,\gamma: Q\to Q$ such that for all $x,y$,

    $$ \alpha(x)\circ \beta(y)=\gamma(x * y). $$

    Isotopy generalizes isomorphism; many properties (like being a Latin square) are preserved under isotopy.

  • Parastrophes: From a quasigroup one can derive up to six related quasigroups by permuting arguments and solving for different variables—these are the parastrophes (e.g., exchanging the roles of left/right division).

  • Moufang and Bol loops: These are loops (quasigroups with identity) that satisfy extra identities weaker than full associativity, giving them group-like behavior in certain contexts.

Key theorem: If a quasigroup is associative, then it has a two-sided identity and inverses, so it is a group. Thus “associative quasigroup” $\iff$ group.

Applications: Quasigroups and their finite versions (Latin squares) appear in design theory (e.g., experimental design), error-correcting codes, cryptography (e.g., quasigroup string transformations / stream ciphers), and combinatorial constructions.

If you’d like, I can show more concrete examples (e.g., construct all quasigroups of small order), explore loops like Moufang loops, or prove the associative-quasigroup-is-group fact. Would you prefer a particular direction?

Is this different from having inverses?

Yes. The quasigroup/division property is weaker and more general than “having inverses” in the usual group sense.

  • In a quasigroup you can always solve $a * x = b$ and $y * a = b$ uniquely for $x,y$; this gives you left- and right-“division” operators, but there need not be any distinguished identity element. So you don’t get a global inverse of an element because “inverse” is only defined relative to an identity.

  • In a loop (a quasigroup with a two-sided identity $e$) you can define for each element $a$:

    • A left inverse $a^{L}$ such that $a^{L} * a = e$,
    • A right inverse $a^{R}$ such that $a * a^{R} = e$. These exist uniquely, but in a general loop $a^{L}$ and $a^{R}$ might be different. If they coincide and behave well (e.g., $(a^{-1})^{-1}=a$, $(a b)^{-1}=b^{-1}a^{-1}$), the loop is said to have the inverse property (many Moufang loops do).
  • In a group (an associative loop) there is an identity and each element has a two-sided inverse $a^{-1}$ with $a^{-1} * a = a * a^{-1} = e$; associativity makes these inverses behave in the familiar algebraic way.

So: quasigroup division gives you the ability to “undo” $a *$ or $* a$ in isolation, but without an identity you can’t talk about the inverse of $a$ itself. Having a (two-sided) inverse is a stronger structure that requires at least a loop, and in full generality (as in groups) also associativity.

Wednesday, August 6, 2025

Associative quasigroups: They are groups

Explain associative quasigroups

Sure. Here’s the picture in full:

1. Definitions

  • A quasigroup $(Q,*)$ is a set with a binary operation $*$ such that for every $a,b\in Q$ the equations $a*x=b$ and $y*a=b$ have unique solutions $x,y\in Q$. Equivalently, left and right multiplication by any fixed element are bijections. (Wikipedia)

  • A loop is a quasigroup that has an identity element $e$ with $e*a = a*e = a$ for all $a$. (Wikipedia)

  • An associative quasigroup is a quasigroup whose operation is associative: $(a*b)*c = a*(b*c)$ for all $a,b,c\in Q$. (groupprops.subwiki.org)

2. Main fact

Any nonempty associative quasigroup is a group. In other words, associativity together with the quasigroup division property forces the existence of a two-sided identity and two-sided inverses, so $(Q,*)$ satisfies all the group axioms. Conversely, every group is trivially an associative quasigroup. (groupprops.subwiki.org, Wikipedia)

(Minor technical note: the empty set can be vacuously an associative quasigroup under some conventions, but it lacks an identity element and so is not a group in the usual sense. The standard theorem refers to nonempty associative quasigroups. ) (Mathematics Stack Exchange)

3. Sketch of the proof (from the standard argument): (groupprops.subwiki.org)

Let $(Q,*)$ be an associative quasigroup and pick some $a\in Q$ (so $Q$ is assumed nonempty).

  • By the quasigroup property, there is a unique $e\in Q$ with $a*e = a$. For an arbitrary $b\in Q$, pick $y$ with $y*a = b$; then

    $$ b*e = (y*a)*e = y*(a*e) = y*a = b $$

    using associativity. Thus $e$ is a right identity for all elements. Likewise one constructs a left identity, and uniqueness of left/right neutral elements forces them to coincide, giving a two-sided identity $e$.

  • Now with identity $e$ in hand, for any $a\in Q$ the quasigroup property gives $x,y$ such that $a*x = e$ and $y*a = e$. Associativity implies any left and right inverse coincide, so each element has a two-sided inverse.

Hence $Q$ has an identity and every element is invertible, and the operation is associative: it is a group. (groupprops.subwiki.org)

4. Consequences / interpretation

So “associative quasigroup” (with nonempty underlying set) is just another way to axiomatize a group: one can define a group as an associative quasigroup, without separately postulating an identity and inverses. Historically some early definitions of groups were phrased that way. (groupprops.subwiki.org)

5. Examples

  • Any group $(G,\cdot)$ is an associative quasigroup because it has associativity and the division property (invertibility gives unique solutions to $a*x=b$ and $y*a=b$). (Wikipedia)
  • There are no new “non-group” examples: if you assume associativity in a quasigroup, you’re forced into a group structure. (groupprops.subwiki.org)

If you’d like, I can also give variants (e.g., what weaker associativity-like identities still force a quasigroup to be a loop/group) or examples of nonassociative quasigroups to contrast. Would you like that?

Tuesday, July 29, 2025

Insurance company profit example

Give a simple numerical example of what an insurance company does to turn a profit

Here’s a very simple toy example showing how an insurance company makes money:

Assumptions

  • You sell 1,000 identical policies.
  • Each policyholder pays a \$100 annual premium.
  • The insurer estimates a 5% chance any one policy will result in a claim, and that the average claim size is \$1,000.
  • The company has other operating costs (salaries, marketing, overhead) equal to 20% of total premiums.

  1. Premium revenue

    $$ 1{,}000\ \text{policies} \times \$100 = \$100{,}000 $$

  2. Expected claims cost

    $$ 1{,}000\ \text{policies} \times 5\% \times \$1{,}000 = \$50{,}000 $$

  3. Operating expenses

    $$ 20\% \times \$100{,}000 = \$20{,}000 $$

  4. Profit

    $$ \underbrace{\$100{,}000}{\text{Premiums}} - \underbrace{\$50{,}000}{\text{Claims}} - \underbrace{\$20{,}000}_{\text{Expenses}} = \$30{,}000 $$


  • Break‑even “loss ratio” here would be claims / premiums = 50%, and expense ratio = 20%.
  • Together they give a combined ratio of 70% (50% + 20%), leaving 30% as profit margin.

In reality, insurers use actuarial models to set premiums so that, over many policies and years, the combined ratio stays below 100%—that gap is their profit.

Friday, July 25, 2025

Singularity

In calculus, what is a singularity?

In calculus (and more broadly in mathematical analysis), a singularity of a function is a point at which the function ceases to be “well‑behaved” in some sense—typically because it is not defined there or its value (or derivative) “blows up” to infinity. More formally:

  • Definition: A point $a$ is called a singularity of a real‑ or complex‑valued function $f(x)$ if $f$ is not analytic (i.e.\ not representable by a convergent power series) in any neighborhood of $a$.

    • In elementary (real) calculus this usually means $f$ is not defined at $a$, or $\lim_{x\to a}f(x)$ does not exist as a finite number.

Types of Singularities (Real Calculus)

  1. Removable singularity − The limit $\lim_{x\to a}f(x)$ exists (finite), but $f(a)$ is either undefined or defined to be something else. − Example:

    $$ f(x) = \frac{\sin x}{x} \quad\text{has}\quad \lim_{x\to0}\frac{\sin x}{x} = 1, $$

    but $f(0)$ is undefined. We “remove” the singularity by defining $f(0)\!=\!1$.

  2. Infinite (or pole‑type) singularity − As $x\to a$, $\lvert f(x)\rvert\to\infty$. − Example:

    $$ f(x) = \frac{1}{x} \quad\text{blows up at}\quad x=0, $$

    so $x=0$ is an infinite singularity (often called a “pole” in complex analysis).

  3. Jump (or essential discontinuity) − The one‑sided limits $\lim_{x\to a^+}f(x)$ and $\lim_{x\to a^-}f(x)$ both exist but are unequal (so the two‑sided limit doesn’t exist). − Example:

    $$ f(x)= \begin{cases} 1, & x<0,\\ 2, & x\ge0, \end{cases} $$

    has a jump at $x=0$.


Singularities in Complex Analysis

When you extend to complex functions $f(z)$, singularities are classified more finely:

  • Removable singularity: $\lim_{z\to a}f(z)$ exists; you can redefine $f(a)$ to make it analytic.
  • Pole: $\lvert f(z)\rvert\to\infty$ as $z\to a$. A pole of order $n$ means $f(z)\sim (z-a)^{-n}$ near $a$.
  • Essential singularity: Neither removable nor pole; $f(z)$ exhibits wild oscillation (Casorati–Weierstrass theorem).

Why Singularities Matter

  • Asymptotic behavior: Vertical asymptotes in graphs arise from infinite singularities.
  • Integration: When performing improper integrals, singularities determine convergence or divergence.
  • Series expansions: Analytic (power‑series) techniques break down at singularities; the distance to the nearest singularity governs a series’ radius of convergence.

Key takeaway: A singularity is simply a point where a function “fails” to be nice—typically by being undefined or unbounded there. Understanding its type (removable, infinite/pole, jump or essential) tells you how you might “fix” it, how the graph behaves near it, and how analytic methods apply around it.

If g and f are injections, so is g ∘ f, and same with bijections

Show that if $f: A\rightarrow B$ and $g: B\rightarrow C$ are injections, so is $g\circ f: A\rightarrow C$.

Recall that a function $h:X\to Y$ is injective (one‐to‐one) precisely when

$$ h(x_1)=h(x_2)\implies x_1=x_2. $$


Theorem. If

$$ f:A\longrightarrow B \quad\text{and}\quad g:B\longrightarrow C $$

are both injective, then their composite

$$ g\circ f:A\longrightarrow C $$

is injective.


Proof. Let $x_1,x_2\in A$ and suppose

$$ (g\circ f)(x_1)\;=\;(g\circ f)(x_2). $$

By definition of composition, this means

$$ g\bigl(f(x_1)\bigr) \;=\; g\bigl(f(x_2)\bigr). $$

Since $g$ is injective, we conclude

$$ f(x_1)=f(x_2). $$

But $f$ is injective, so from $f(x_1)=f(x_2)$ it follows that

$$ x_1 = x_2. $$

Thus

$$ (g\circ f)(x_1)=(g\circ f)(x_2) \;\Longrightarrow\; x_1=x_2, $$

showing $g\circ f$ is injective. $\boxed{}$


Alternative (via contrapositive). One can also argue:

  • If $x_1\neq x_2$, then since $f$ is injective, $f(x_1)\neq f(x_2)$.
  • Then since $g$ is injective, $g\bigl(f(x_1)\bigr)\neq g\bigl(f(x_2)\bigr)$.
  • Hence $(g\circ f)(x_1)\neq(g\circ f)(x_2)$.

This directly shows “distinct inputs give distinct outputs,” i.e.\ $g\circ f$ is one‐to‐one.

Separate conversation with o4-mini:

Show that if $f: A\rightarrow B$ and $g: B\rightarrow C$ are bijections, so is $g\circ f: A\rightarrow C$.

Proof. Suppose

$$ f:A\to B\quad\text{and}\quad g:B\to C $$

are both bijections. We must show that $g\circ f:A\to C$ is bijective, i.e. both injective and surjective.


1. Injectivity

Take any $a,a'\in A$ and assume

$$ (g\circ f)(a)\;=\;(g\circ f)(a'). $$

Then

$$ g\bigl(f(a)\bigr)\;=\;g\bigl(f(a')\bigr). $$

Since $g$ is injective, it follows that

$$ f(a)\;=\;f(a'). $$

But $f$ is injective, so from $f(a)=f(a')$ we conclude

$$ a\;=\;a'. $$

Hence $g\circ f$ is injective.


2. Surjectivity

Let $c\in C$ be arbitrary. Since $g$ is surjective, there exists some

$$ b\in B\quad\text{such that}\quad g(b)=c. $$

Next, because $f$ is surjective, there exists

$$ a\in A\quad\text{such that}\quad f(a)=b. $$

Therefore

$$ (g\circ f)(a) \;=\; g\bigl(f(a)\bigr) \;=\; g(b) \;=\; c. $$

Since $c$ was arbitrary in $C$, this shows $g\circ f$ is surjective.


3. Conclusion

Having established that $g\circ f$ is both injective and surjective, we conclude that

$$ g\circ f:A\;\longrightarrow\;C $$

is a bijection. Moreover, one can check directly that its inverse is

$$ \bigl(g\circ f\bigr)^{-1} \;=\; f^{-1}\circ g^{-1}, $$

providing an explicit bijective inverse.

No set of all ordered pairs, or of all functions

Explain this: Where an ordered pair (x, y) is a set $ \{x, \{x, y\}\} $, the class of all ordered pairs is not a set.

In ZF (and related) set‐theories, one often defines the ordered pair $(x,y)$ à la Kuratowski by

$$ (x,y)\;=\;\{\,x,\;\{x,y\}\}. $$

Let’s call

$$ P \;=\;\bigl\{\,w : \exists\,x,y\bigl[w=\{x,\{x,y\}\}\bigr]\bigr\} $$

the class of all such ordered pairs. We claim $P$ cannot itself be a set but must be a proper class. Here’s the standard argument:


1. Suppose for contradiction that $P$ is a set.


2. Define the first‐projection map

$$ \pi_1\bigl(\{x,\{x,y\}\}\bigr)\;=\;x. $$

Because $\pi_1$ is a definable “function” on ordered pairs, the Axiom Schema of Replacement tells us that

$$ \pi_1[P]\;=\;\{\,x : \exists\,y\bigl((x,y)\in P\bigr)\} $$

must also be a set.


3. But $\pi_1[P]$ is in fact the universe of all sets.

  • For every set $x$, just take $y=x$. Then $\bigl(x,x\bigr)$ is an ordered pair, so belongs to $P$.
  • Hence every $x$ appears as the first coordinate of something in $P$.

Thus

$$ \pi_1[P]\;=\;\{\,x : x\text{ is a set}\}\;=\;V, $$

the class of all sets.


4. But in ZF the universe $V$ is not a set.

If $V$ were a set, one would immediately violate Cantor’s theorem (e.g.\ its power set $\mathcal P(V)$ would have strictly larger cardinality than $V$ itself), or more directly it contradicts the axiom of foundation and the cumulative‐hierarchy conception. In any case, the theory forbids “the set of all sets.”


5. Conclusion

Since assuming $P$ is a set forces $V$ to be a set (via Replacement), which is impossible, we must reject the assumption. Therefore

The class of all Kuratowski‐ordered pairs is too large to be a set; it is a proper class.


Alternative (union) argument

One can also observe that

$$ \bigcup\!\bigl(\bigcup P\bigr) \,=\, \bigcup\,\bigl\{\,x,\{x,y\}:x,y\in V\bigr\} \,=\,V. $$

If $P$ were a set then so would be its double–union $\bigcup\bigl(\bigcup P\bigr)$, again forcing $V$ to be a set. Same contradiction.

A set f is called a function if all its elements are ordered pairs and for each set a there is at most one set b such that that $(a, b) \in f$. Show that the class of all functions is not a set.

Here is a direct “size‐too‐large” argument showing the class

$$ \mathcal F \;=\;\{\,f : f \text{ is a function (i.e.\ a set of ordered pairs with unique first–coordinates)}\} $$

cannot itself be a set in ZF.


1. For each set $a$, cook up a tiny function $f_{a}$

Define

$$ f_{a} \;=\; \{\,(a,a)\,\}. $$

Clearly:

  1. $(a,a)$ is an ordered pair (e.g.\ Kuratowski’s $\{a,\{a,a\}\}$),
  2. $f_{a}$ is a set of ordered pairs,
  3. no two pairs in $f_{a}$ share the same first coordinate. Hence $f_{a}\in\mathcal F$.

2. If $\mathcal F$ were a set, then so would be $\bigcup\!\bigcup\mathcal F$

  • By the Axiom of Union, if $\mathcal F$ is a set then $\displaystyle U:=\bigcup\mathcal F$ is a set (the union of all functions).
  • Then again by Union, $\displaystyle \bigcup U=\bigcup\!\bigl(\bigcup\mathcal F\bigr)$ is also a set.

3. But $\bigcup\!\bigl(\bigcup\mathcal F\bigr)=V$, the universe of all sets

  1. First, $U=\bigcup\mathcal F$ contains every ordered‐pair component: for each $f_{a}$ we have $(a,a)\in f_{a}\subset U$, so

    $$ U\;\supset\;\{a,\{a,a\}\} \;=\;\{a,\{a\}\}. $$

  2. Now

    $$ \bigcup U \;=\; \bigcup\bigl\{\{a,\{a\}\}:a\in V\bigr\} \;=\; \bigl\{\,a : a\in V\bigr\}\;\cup\;\bigl\{\,x : x\in\{a\}\text{ for some }a\in V\bigr\} \;=\; V. $$

    Concretely, every set $a$ appears among the first‐coordinates, and every set $x$ appears as the unique member of $\{a\}$ for some $a$.

Hence if $\mathcal F$ were a set, we would have built the set $\bigcup\!\bigl(\bigcup\mathcal F\bigr)$, which must equal $V$. But ZF forbids $V$ from being a set (one way to see this is Cantor’s theorem: $\mathcal P(V)$ would strictly exceed $V$ in size).


4. Conclusion

Because assuming $\mathcal F$ is a set forces $V$ to be a set, we reach a contradiction. Therefore

The class of all functions is not a set; it is a proper class.

Wednesday, July 23, 2025

Why 2^923 mod 15 = 8 (in-depth)

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. Please find the remainder when 2^923 is divided by 15.

To compute $2^{923}\bmod15$, note that $\gcd(2,15)=1$ and Euler’s totient $\varphi(15)=8$, so

$$ 2^8\equiv1\pmod{15}. $$

Thus

$$ 2^{923}\equiv2^{923\bmod8}\equiv2^3\equiv8\pmod{15}. $$

Answer: The remainder is $8$.

Thank you for finding the remainder, but I don’t understand what you said, please explain this “Euler’s totient φ” business.

Sure! Let me break it down step by step.


1. What is Euler’s totient function, $\varphi(n)$?

  • $\varphi(n)$ is the number of positive integers up to $n$ that are coprime to $n$ (i.e.\ they share no prime factors with $n$).

  • For example, to compute $\varphi(15)$:

    1. Factor $15 = 3 \times 5$.

    2. Use the formula for a product of two distinct primes:

      $$ \varphi(p\cdot q) \;=\; (p-1)\,(q-1). $$

    3. Thus

      $$ \varphi(15) \;=\;(3-1)\,(5-1)\;=\;2\times4\;=\;8. $$

    So there are 8 numbers in $\{1,2,\dots,15\}$ that are coprime to 15: $1,2,4,7,8,11,13,14$.


2. Euler’s theorem (a generalization of Fermat’s little theorem)

Statement. If $\gcd(a,n)=1$, then

$$ a^{\varphi(n)} \;\equiv\;1 \pmod{n}. $$

  • “$\gcd(a,n)=1$” means $a$ and $n$ have no common prime factors.
  • So raising $a$ to the $\varphi(n)$-th power always gives a remainder of 1 when divided by $n$.

3. Applying it to $2^{923}\bmod15$

  1. Check that $\gcd(2,15)=1$. ✔

  2. Compute $\varphi(15)=8$.

  3. By Euler’s theorem,

    $$ 2^8 \equiv 1 \pmod{15}. $$

  4. Therefore powers of 2 modulo 15 repeat every 8 steps. In particular,

    $$ 2^{923} \;=\; 2^{8\cdot115 + 3} \;=\; \bigl(2^8\bigr)^{115}\;\times\;2^3 \;\equiv\; 1^{115}\;\times\;2^3 \;=\; 2^3 \;=\; 8 \;\pmod{15}. $$

  5. Remainder = 8.


4. Why this helps

Without Euler’s theorem, you’d have to multiply 2 by itself 923 times and reduce mod 15 each time—impractical by hand. The totient function tells you exactly when the pattern of remainders resets.

I hope this clarifies what $\varphi(n)$ is and how Euler’s theorem lets us simplify huge exponents!

What’s the reasoning for going from $2^8\equiv1\pmod{15}$ to $2^{923}\equiv2^{923\bmod8}\pmod{15}$?

The key property you’re using is that if you know

$$ a^M \equiv 1 \pmod{n}, $$

then for any exponent $E$ you can write $E$ in the division‐with‐remainder form

$$ E = M\cdot Q + R,\quad 0 \le R < M, $$

and get

$$ a^E = a^{M\cdot Q + R} = \bigl(a^M\bigr)^Q \cdot a^R \equiv 1^Q \cdot a^R \;=\;a^R \pmod{n}. $$


Applying it to our case

  1. We know $2^8 \equiv 1 \pmod{15}$.

  2. Divide the exponent 923 by 8:

    $$ 923 = 8 \times 115 + 3, $$

    so here $M = 8$, $Q = 115$, $R = 3$.

  3. Then

    $$ 2^{923} = 2^{8\cdot115 + 3} = \bigl(2^8\bigr)^{115}\times 2^3 \equiv 1^{115}\times 2^3 = 2^3 \pmod{15}. $$

  4. Hence you only need to compute $2^3\equiv8$, and you never have to multiply out all 923 factors.


So whenever you have a “cycle” $a^M\equiv1\pmod{n}$, you can reduce the exponent modulo $M$. That’s exactly why

$$ 2^{923}\equiv 2^{923\bmod8}\pmod{15}. $$

How do we know $\varphi(p\cdot q) \;=\; (p-1)\,(q-1)$?

There are two classic ways to see why, for two distinct primes $p$ and $q$,

$$ \varphi(pq)\;=\;(p-1)(q-1). $$


1. Multiplicativity of $\varphi$

Euler’s totient function is multiplicative on coprime arguments:

$$ \text{if }\gcd(m,n)=1,\quad\text{then }\varphi(mn)=\varphi(m)\,\varphi(n). $$

Since $p$ and $q$ are primes, $\gcd(p,q)=1$, and we know

$$ \varphi(p)=p-1,\quad \varphi(q)=q-1 $$

(because out of the $p$ numbers $1,2,\dots,p$, exactly the multiples of $p$ — just “$p$” itself — fail to be coprime, leaving $p-1$ survivors; similarly for $q$). Hence

$$ \varphi(pq) =\varphi(p)\,\varphi(q) =(p-1)(q-1). $$


2. Direct “inclusion–exclusion” count

Count how many integers $1\le k\le pq$ are not coprime to $pq$. Exactly those that share a factor with $pq$, i.e.\ those divisible by $p$ or by $q$:

  • Multiples of $p$ in that range: $\displaystyle \frac{pq}{p}=q$ of them ($p,2p,\dots,qp$).
  • Multiples of $q$ in that range: $\displaystyle \frac{pq}{q}=p$ of them ($q,2q,\dots,pq$).
  • But we’ve counted multiples of both $p$ and $q$ (i.e.\ multiples of $pq$) twice; there is exactly 1 of those ($pq$ itself).

By inclusion–exclusion, the total count of “bad” numbers is

$$ \#\{\text{divisible by }p\} + \#\{\text{divisible by }q\} - \#\{\text{divisible by both}\} = q + p - 1. $$

Since there are $pq$ numbers in all, the count of good (coprime) ones is

$$ pq - (q + p -1) = pq - p - q + 1 = (p-1)(q-1). $$

Either viewpoint immediately yields

$$ \boxed{\varphi(pq)=(p-1)(q-1).} $$

How is Euler’s theorem proved?

Here is a classic, elementary proof of Euler’s theorem:


Setup: the reduced residue system mod $n$

Let

$$ n>1,\quad a\in\mathbb{Z},\quad\gcd(a,n)=1. $$

Define the reduced residue system modulo $n$ as the set

$$ R \;=\;\{\,r_1,r_2,\dots,r_{\varphi(n)}\} $$

of all positive integers $<n$ that are coprime to $n$. By definition, $|R|=\varphi(n)$.


Proof by “permutation of residues”

  1. Multiply each element of $R$ by $a$, and reduce modulo $n$. Because $\gcd(a,n)=1$, none of the products

    $$ a\,r_1,\;a\,r_2,\;\dots,\;a\,r_{\varphi(n)} $$

    can share a nontrivial factor with $n$. Hence each $a\,r_i\pmod n$ is again in the reduced residue system.

  2. No collisions. If for two indices $i\neq j$ we had

    $$ a\,r_i \equiv a\,r_j \pmod n, $$

    then

    $$ a\,(r_i - r_j)\equiv0\pmod n \quad\Longrightarrow\quad n\bigm|\bigl(r_i-r_j\bigr), $$

    which forces $r_i\equiv r_j\pmod n$. But $0<r_i,r_j<n$, so $r_i=r_j$, contradicting $i\neq j$. Thus multiplication by $a$ permutes $R$.

  3. Compare the two products. Consider the product of all members of $R$:

    $$ P \;=\; r_1\,r_2\cdots r_{\varphi(n)}. $$

    On one hand, that’s $P$. On the other hand, because the list $\{a\,r_i\pmod n\}$ is just a re‑ordering of the same $r_i$’s, its product is also

    $$ a^{\varphi(n)}\,P \;\equiv\; P\pmod n. $$

    Hence

    $$ a^{\varphi(n)}\,P \;\equiv\;P\pmod n \quad\Longrightarrow\quad a^{\varphi(n)}\equiv1\pmod n \quad\Bigl(\gcd(P,n)=1\Bigr). $$

That completes the proof.


Alternate (group‑theoretic) viewpoint

The integers coprime to $n$, under multiplication mod $n$, form a finite group of order $\varphi(n)$. In any finite group, Lagrange’s theorem says that the order of any element divides the group order, and in particular $x^{|G|}=e$. Taking $x=[a]$ in the group $(\mathbb{Z}/n\mathbb{Z})^\times$ shows

$$ [a]^{\varphi(n)} = [1] \quad\Longleftrightarrow\quad a^{\varphi(n)}\equiv1\pmod n. $$

Why 3^2463 ≡ 3 (mod 8)

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. Please find the remainder when 3^2463 is divided by 8.

To compute $3^{2463}\bmod8$, observe the pattern of powers of 3 modulo 8:

$$ \begin{aligned} 3^1 &\equiv 3 \pmod{8},\\ 3^2 &\equiv 9\equiv1 \pmod{8},\\ 3^3 &\equiv3^2\cdot3\equiv1\cdot3\equiv3\pmod{8},\\ 3^4 &\equiv(3^2)^2\equiv1^2\equiv1\pmod{8}, \end{aligned} $$

so the residues cycle with period 2:

$$ 3^{\text{odd}}\equiv3,\quad 3^{\text{even}}\equiv1\pmod8. $$

Since $2463$ is odd,

$$ 3^{2463}\equiv3\pmod8. $$

Answer: The remainder when $3^{2463}$ is divided by 8 is $\boxed{3}$.

Why do we expect that this pattern always holds?

The reason the pattern

$$ 3^1\equiv3,\quad 3^2\equiv1,\quad 3^3\equiv3,\quad3^4\equiv1,\;\dots $$

repeats every two exponents comes down to the single fact

$$ 3^2 = 9 = 8 + 1 \;\;\Longrightarrow\;\; 3^2 \equiv 1 \pmod8. $$

Once you know that $3^2\equiv1\pmod8$, it follows immediately that for any integer $k$,

  • $$ 3^{2k}=(3^2)^k\equiv1^k\equiv1\pmod8, $$
  • $$ 3^{2k+1}=3\cdot3^{2k}\equiv3\cdot1\equiv3\pmod8. $$

Thus the residues must alternate $3,1,3,1,\dots$ with period 2.


A bit more structure: the multiplicative group of units modulo 8

  • The units in $\mathbb{Z}/8\mathbb{Z}$ are those integers coprime to 8, namely $\{1,3,5,7\}$.

  • This set forms a finite group under multiplication mod 8, whose order is $\varphi(8)=4$.

  • By Lagrange’s theorem, the order (the size) of any element in a finite group must divide the size of the group. Here:

    $$ \text{order}(3)\;|\;4. $$

  • We check directly that $3^2\equiv1\pmod8$, so the order of 3 is exactly 2, and hence its powers repeat with period 2.

Either way—whether by the elementary identity $3^2\equiv1\pmod8$ or by invoking the structure of $(\mathbb{Z}/8\mathbb{Z})^\times$—you see why the cycle length is 2, and why $3^{2463}\equiv3$.

I’m not sure why ChatGPT wrote “period 2” with a narrow no-break space (NNBSP) instead of a regular space (as in “period 2”).

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

Motivation for decomposition into even and odd functions

Every function can be expressed uniquely as the sum of an even function and an odd function, like so: let E(x) be (f(x)+f(-x))/2, and let O(x) be (f(x)-f(-x))/2. Then E(x) is even, O(x) is odd, and f(x) = E(x) + O(x).

But what’s the point of doing this?

Decomposing any function $f$ into its even and odd parts

$$ E(x) = \frac{f(x)+f(-x)}2,\quad O(x)=\frac{f(x)-f(-x)}2,\quad f=E+O $$

is more than just a neat algebraic trick. Here’s why it’s so useful:


1. Exploiting symmetry in integrals

  • Odd integrals vanish: if $O$ is odd, then

    $$ \int_{-a}^a O(x)\,dx = 0. $$

    So

    $$ \int_{-a}^a f(x)\,dx = \int_{-a}^a E(x)\,dx + \underbrace{\int_{-a}^a O(x)\,dx}_{0} = 2\int_0^a E(x)\,dx. $$

    You’ve halved your work by reducing to the even part only.


2. Fourier series simplification

  • An arbitrary periodic $f$ has a Fourier series

    $$ f(x)=a_0 + \sum_{n=1}^\infty\bigl[a_n\cos(nx)+b_n\sin(nx)\bigr]. $$

  • The cosine terms are the even part; the sine terms are the odd part.

  • You can immediately tell which coefficients vanish if you know $f$ is purely even or purely odd.


3. Power-series (Taylor) clarity

  • Expanding about zero, an even function has only even powers:

    $$ E(x)=c_0 + c_2 x^2 + c_4 x^4 + \cdots, $$

    while an odd function has only odd powers:

    $$ O(x)=c_1 x + c_3 x^3 + \cdots. $$

  • Decomposition helps identify which terms must be present or zero in your series.


4. Solving differential equations

  • Many differential operators commute with the “flip” $x\mapsto -x$, so they preserve evenness/oddness.
  • You can split a boundary-value or initial-value problem into two simpler problems—one on the even subspace, one on the odd—often halving the complexity.

5. Linear-algebra viewpoint

  • The space of all functions (say, on $\mathbb{R}$) decomposes as

    $$ \{\,\text{even}\}\ \oplus\ \{\text{odd}\}. $$

  • Projection onto each subspace is exactly $E$ and $O$. This is a concrete example of projecting onto complementary subspaces in any vector space.


6. Practical computations & modeling

  • In physics and engineering, boundary conditions often enforce symmetry. If you know a solution must be even (or odd), you can discard half the domain or simplify the ansatz immediately.
  • In signal processing, even/odd decomposition relates to splitting a signal into its symmetric and antisymmetric components, each of which may carry different information.

In short

Breaking $f$ into $E+O$ lets you

  1. Harness symmetry to simplify integrals and equations.
  2. Organize expansions (Fourier, Taylor) by parity.
  3. Project cleanly onto invariant subspaces.

It’s a fundamental tool whenever symmetry—or the lack of it—can be used to your advantage.

Friday, July 11, 2025

Primes using Fermat numbers

Define the nth Fermat number, Fn, by $F_n =2^{2^n}+1$ for n = 0, 1, 2, 3, …. The first few Fermat numbers are F0 = 3, F1 = 5, F2 = 17, F3 = 257.

Prove by induction that F0 ⋅ F1⋯Fn-1 + 2 = Fn, for n ≥ 1.

Proof by induction on $n\ge1$.


Base case ($n=1$)

We must check that

$$ F_0 + 2 = F_1. $$

Since $F_0 = 2^{2^0}+1 = 2+1 = 3$ and $F_1 = 2^{2^1}+1 = 4+1 = 5$, we have

$$ F_0 + 2 = 3 + 2 = 5 = F_1, $$

so the statement holds for $n=1$.


Inductive step

Inductive hypothesis. Suppose for some $k\ge1$ that

$$ F_0 \,F_1 \,\cdots\, F_{k-1} \;+\; 2 \;=\; F_k. $$

Equivalently,

$$ F_0 F_1 \cdots F_{k-1} \;=\; F_k \;-\; 2. $$

We must show

$$ F_0 F_1 \cdots F_{k-1}\,F_k \;+\; 2 \;=\; F_{k+1}. $$

Starting from the left‐hand side,

$$ F_0 F_1 \cdots F_{k-1}\,F_k \;+\; 2 \;=\;\bigl(F_k - 2\bigr)\,F_k \;+\; 2 \quad(\text{by the inductive hypothesis}). $$

Compute

$$ (F_k - 2)\,F_k =F_k^2 \;-\;2\,F_k =\bigl(2^{2^k}+1\bigr)^2 \;-\;2\bigl(2^{2^k}+1\bigr). $$

Expand:

$$ \bigl(2^{2^k}+1\bigr)^2 =2^{2\cdot2^k} \;+\;2\cdot2^{2^k}\;+\;1 =2^{2^{\,k+1}} \;+\;2^{2^k+1}\;+\;1, $$

so

$$ F_k^2 -2F_k =\bigl(2^{2^{k+1}} +2^{2^k+1}+1\bigr) \;-\;\bigl(2^{2^k+1}+2\bigr) =2^{2^{k+1}} \;-\;1. $$

Thus

$$ F_0 F_1 \cdots F_{k-1}\,F_k +2 =\bigl(F_k^2 -2F_k\bigr)+2 =\bigl(2^{2^{k+1}} -1\bigr)+2 =2^{2^{k+1}}+1 =F_{k+1}. $$

This completes the inductive step.


By the principle of mathematical induction, for every integer $n\ge1$,

$$ F_0\,F_1\cdots F_{n-1} +2 \;=\; F_n, $$

as claimed. ∎

Use this formula to prove that there are an infinite number of primes, by showing that no two Fermat numbers have any prime factors in common.

Here’s the standard argument.


1. Fermat numbers are pairwise coprime

Recall from the induction you’ve already proved that, for every $n\ge1$,

$$ F_n \;=\; F_0\,F_1\cdots F_{n-1} \;+\;2. $$

Fix two indices $0\le m<n$. Suppose $p$ is a prime dividing both $F_m$ and $F_n$. Then:

  1. Since $p\mid F_n$ and

    $$ F_n = F_0F_1\cdots F_{n-1} +2, $$

    we have

    $$ F_0F_1\cdots F_{n-1} \;+\;2 \;\equiv\;0\pmod p \quad\Longrightarrow\quad F_0F_1\cdots F_{n-1}\;\equiv\;-2\pmod p. $$

  2. But $p\mid F_m$ for some $m<n$, so $p$ divides the product $F_0F_1\cdots F_{n-1}$. Hence

    $$ F_0F_1\cdots F_{n-1}\;\equiv\;0\pmod p. $$

  3. Combining the two congruences,

    $$ 0 \;\equiv\; F_0\cdots F_{n-1}\;\equiv\;-2\pmod p \quad\Longrightarrow\quad p\mid2. $$

    Thus $p=2$. But every Fermat number $F_k = 2^{2^k}+1$ is odd, so cannot be divisible by 2.

Therefore no prime $p$ can divide both $F_m$ and $F_n$ for $m<n$. Equivalently,

$$ \gcd(F_m,\,F_n)\;=\;1 \quad\text{whenever }m\neq n. $$


2. Infinitely many primes

Since each Fermat number $F_n$ is greater than 1, it has at least one prime divisor. By the argument above, no prime divisor of $F_n$ can ever appear among the prime divisors of any other $F_m$ with $m\neq n$. Hence each $F_n$ contributes at least one new prime that we have never seen before. As $n$ ranges over the nonnegative integers, we thus get infinitely many distinct primes. ∎

Monday, July 7, 2025

Motivation of matrix multiplication

Inspired by this tweet.

Explain this claim: "matrix multiplication is Like That because it is a definition of multiplication over matrices that satisfies some of the same properties we care about over real numbers"

Matrix multiplication isn’t “mystical” or arbitrary—it’s chosen so that when you represent linear transformations as matrices, composing those transformations corresponds exactly to multiplying their matrices, and so that the algebraic rules you like for real numbers carry over. Here’s why the definition looks the way it does:


1. Matrices as linear maps

  • Interpretation An $m\times n$ matrix $A$ represents a linear map

    $$ A: \mathbb R^n \to \mathbb R^m, $$

    sending each vector $x\in\mathbb R^n$ to $A,x\in\mathbb R^m$.

  • Composition If you then apply another linear map $B: \mathbb R^p \to \mathbb R^n$ after $A$, you get

    $$ A\circ B: \mathbb R^p \xrightarrow{,B,} \mathbb R^n \xrightarrow{,A,} \mathbb R^m. $$

    We want the matrix of $A\circ B$ to be exactly the product of the matrices of $A$ and $B$.


2. Desired algebraic properties

To mirror the familiar arithmetic of real numbers, we impose the following on “multiplication”:

  1. Associativity $(A,B),C = A,(B,C)$ so you can compose several maps without worrying about how you parenthesize.

  2. Distributivity $A,(B + C) = A,B + A,C,\quad (A + B),C = A,C + B,C$ so matrix multiplication plays nicely with matrix addition.

  3. Identity element There should be an “$I$” so that $I,A = A = A,I,$ corresponding to the identity map on $\mathbb R^n$.

  4. Compatibility with scalars $(\lambda A),B = \lambda,(A,B) = A,(\lambda B)$ for any real $\lambda$.

Together, these axioms make the set of all $n\times n$ matrices into a ring (in fact, a non-commutative algebra) much like the real numbers.


3. Deriving the “row-by-column” rule

Let

$$ A = \bigl[a_{ij}\bigr]{m\times n},\quad B = \bigl[b{jk}\bigr]_{n\times p}. $$

We want the $(i,k)$-entry of the product $C = A,B$ to be exactly the result of doing:

  1. Take the $i$th row of $A$: $\bigl(a_{i1},a_{i2},\dots,a_{in}\bigr)$.

  2. Take the $k$th column of $B$: $\bigl(b_{1k},b_{2k},\dots,b_{nk}\bigr)^{T}$.

  3. Compute their dot-product:

    $$ c_{ik} ;=; \sum_{j=1}^n a_{ij},b_{jk}. $$

Why a dot-product? Because applying $B$ to the $k$th standard basis vector $e_k$ picks out its $k$th column, and then $A$ acts on that vector by taking those linear combinations of its rows.


4. What you “get for free”

Because of this definition:

  • Composition ↔ multiplication The matrix of $A\circ B$ is $A,B$.

  • All the ring axioms hold You can add, multiply, and scale matrices just like numbers, except multiplication may not commute ($A,B\neq B,A$ in general).

  • Powerful algebraic structure You can talk about invertibility (when there’s a two-sided “matrix reciprocal”), eigen-theory, determinant, trace, etc., all of which generalize properties of numbers.


In a nutshell

Matrix multiplication is “like that” because it’s defined to be the operation on arrays of numbers which (a) models composition of linear maps and (b) satisfies the familiar algebraic laws (associativity, distributivity, identity, scalar compatibility) we depend on when working with real numbers.

Saturday, July 5, 2025

Motivation for the definition of sine

what's the motivation for letting sin(x) be defined for angles greater than 90°? how is it defined without a right triangle?

In elementary geometry, sine is introduced via a right-triangle: for an acute angle θ,

$$ \sin θ = \frac{\text{opposite side}}{\text{hypotenuse}}\,.
$$

But once you want to talk about angles beyond 90° (or even negative angles, or angles > 360°, or non-geometric “angles” like radians), that triangle picture no longer suffices. Here’s the motivation and the standard ways we extend sin to all real (and even complex) arguments:


1. Motivation for extending beyond 90°

  1. Continuity & periodicity If you try to piece together right-triangle definitions only for 0°–90°, you’d have gaps and “jumps” when you pass 90°, 180°, etc. But in nature (waves, rotations, oscillations) the sine-wave is smooth and repeats forever. Extending sin θ to every real θ makes it a continuous, infinitely differentiable, 2π-periodic function.

  2. Applications in physics & engineering Rotations in the plane (e.g. turning a vector by an arbitrary angle), harmonic motion, signal processing, Fourier analysis—all require sine (and cosine) at angles of any size, not just acute ones.

  3. Solving differential equations The basic ODE

    $$ \frac{d^2y}{dx^2} + y = 0 $$

    has solutions y = sin x and y = cos x defined for all real x. Restricting to 0–90° would deprive us of half of the solutions!


2. The unit‐circle definition

We place ourselves in the Cartesian plane and draw the unit circle (radius 1 centered at the origin). Then for any real angle x (measured, say, in radians, counter-clockwise from the positive-x axis):

  • Travel around the circle by arc‐length x.

  • Let (X,Y) be the coordinates of the point you land on.

  • Define

    $$ \cos(x) = X,\quad \sin(x) = Y. $$

This single picture handles:

  • x between 0 and π/2 (0°–90°) → first quadrant
  • x between π/2 and π (90°–180°) → second quadrant (sin is still positive, cos flips negative)
  • x between π and 3π/2 → third quadrant (both sin, cos negative)
  • and so on, for all real x, positive or negative, even beyond 2π.

3. The power‐series (analytic) definition

Analytically, we can define sine by its infinite Taylor series about 0:

$$ \boxed{\displaystyle \sin x = x \;-\;\frac{x^3}{3!}\;+\;\frac{x^5}{5!}\;-\;\frac{x^7}{7!} \;+\;\cdots} $$

This converges for every real (and complex!) x. You no longer need a triangle or circle—just algebra and limits. From this you can prove all the familiar properties:

  • Oddness: sin(−x)=−sin x
  • Periodicity: sin(x+2π)=sin x
  • Derivatives: d/dx sin x=cos x, etc.

4. The differential‐equation definition

Yet another equivalent is to say “sin x is the unique solution of”

$$ \frac{d^2y}{dx^2} + y = 0,\quad y(0)=0,\;y'(0)=1. $$

This defines it for all x, independent of any triangle picture.


In practice

  • For intuition: think “y‐coordinate on the unit circle.”
  • For computation: use the series or numerical algorithms derived from it.
  • For theory: you can lean on the ODE or on complex‐analytic continuation to define sin z for complex z.

All of these views agree and ensure that sin x makes perfect sense (and behaves beautifully) for angles of any measure—acute, obtuse, reflex, negative, fractional, or purely imaginary.

The output contained a hallucinated imgur image. I only saw it after exporting it for this post. I have commented it out, but you can see it in the HTML of this page.

Wednesday, July 2, 2025

Presuppositions, implicatures, and implications

This first response is from o4-mini.

In pragmatics what are presuppositions and implicatures?

In pragmatics, both presuppositions and implicatures are ways in which speakers convey—and listeners infer—information that goes beyond the literal meaning of utterances. They differ, however, in how that extra information is encoded and recovered.


Presuppositions

A presupposition is an assumption that a sentence (or utterance) takes for granted as background information. If the presupposition isn’t already established in the context, the utterance typically feels odd, misleading, or infelicitous.

  • Definition: A proposition P is a presupposition of sentence S if, whenever S is used felicitously, the speaker and hearer must both accept P as true.

  • Triggers: Certain words or constructions automatically carry presuppositions, for example:

    • Definite descriptions: “The king of France is bald.” presupposes “There is a king of France.”
    • Factives: “I regret that I ate too much.” presupposes “I ate too much.”
    • Change-of-state verbs: “She stopped smoking.” presupposes “She used to smoke.”
    • Temporal clauses: “Before he arrived, we had dinner.” presupposes “He arrived.”
  • Projection behavior: Even in negated, questioned, or conditional forms, presuppositions tend to “project”—that is, they remain assumptions regardless of operators around them.

    • “Did you stop smoking?” still presupposes “You used to smoke.”
    • “If she stopped smoking, she will feel better.” presupposes “She used to smoke.”
    • “She didn’t regret leaving.” still presupposes “She left.”
  • Example:

    “John’s sister is a doctor.”

    • Presupposes: “John has a sister.”
    • As a result, if you don’t already believe John has a sister, the utterance feels strange or misleading.

Implicatures

An implicature is information implicitly conveyed by a speaker, which the listener infers based on principles of cooperative communication rather than any explicit presupposition.

There are two main types:

  1. Conversational implicatures (Gricean implicatures)
  2. Conventional implicatures

1. Conversational Implicatures

  • Definition: Inferred meanings arising from the assumption that speakers generally follow conversational maxims (Quantity, Quality, Relation, Manner) laid out by Grice (1975).

  • Mechanism: Listeners reason: “If the speaker meant X, they would have said so; since they didn’t, they must mean Y.”

  • Example (Quantity):

    A: “How was the movie?” B: “Well, the cinematography was nice.”

    Implicature: B didn’t comment on plot or acting ⇒ perhaps the plot/acting was not so good.

  • Example (Relation):

    A: “Do you want to go to the concert?” B: “I have to work early tomorrow.”

    Implicature: B is declining the invitation, because they’re using relevance to imply they can’t attend.

  • Cancelability: Conversational implicatures can be explicitly “canceled” without contradiction:

    “The movie was great—and by the way, the acting was fantastic too.”


2. Conventional Implicatures

  • Definition: Meanings tied to particular words or constructions that are not part of the truth‐conditional content but are still conventionally attached to them.

  • Properties:

    • Encoded in the lexicon or grammar (unlike conversational implicatures, they do not depend on context-driven inference).
    • Non‐cancellable (or at least far harder to cancel felicitously).
    • Non‐projecting in the same way as presuppositions, but they persist even under negation.
  • Common triggers:

    • But, yet, however (contrast)
    • Even (scalar emphasis)
    • So (conclusion)
    • Because (reason)
  • Example:

    “She’s poor but happy.”

    • Truth‐conditions: ¬(rich(she)) ∧ happy(she)
    • Conventional implicature: Contrast between “being poor” and “being happy.”

Key Differences

Aspect Presupposition Conversational Implicature Conventional Implicature
Source Triggered by certain words/structures Inferred via Cooperative Principle Lexical/grammatical
Dependence on context Requires context to be satisfied Requires context and maxims Largely context‐independent
Cancelable? Generally non‐cancelable (except by defeating) Cancelable Typically non‐cancelable
Projection behavior Projects under negation, questioning, etc. Does not project; tied to particular context Persists under negation but not “projecting” like presuppositions

Why They Matter

Understanding presuppositions and implicatures is crucial for:

  • Pragmatic inference: How we read between the lines in everyday conversation.
  • Discourse analysis: Tracking which assumptions are taken for granted and how speakers manage shared information.
  • Computational linguistics: Designing systems that can interpret user intent beyond the literal text.

By distinguishing what must already be known (presuppositions) from what is conveyed by conversational strategy (implicatures), we gain insight into the rich, subtle ways language users communicate more than meets the eye.

This second response is from o3.

Expand this comparison to also compare logical implication to these two

Below is an expanded three-way comparison that brings logical implication into the picture alongside presuppositions and implicatures. After a quick refresher on what logical implication is, I contrast the three phenomena along the dimensions that typically matter in pragmatics and logic.


1. What is logical implication?

In formal semantics and logic, implication (often symbolized “→”) is a truth-functional relation between propositions.

  • Definition (classical logic): ϕ → ψ is false only when ϕ is true and ψ is false; in all other truth-value combinations it is true.
  • Speaker commitment: Uttering a statement that contains an implication (e.g. “If it rains, the streets will be wet”) commits the speaker to the truth of that whole conditional—but not to the truth of its antecedent or consequent individually.
  • Context-independence: The truth value depends purely on the propositions involved, not on what interlocutors mutually assume or infer.

2. Side-by-side comparison

Feature / Question Presupposition Conversational Implicature Conventional Implicature Logical Implication
How introduced? By specific triggers (definites, factives, “stop,” etc.) Inferred via Grice’s Cooperative Principle and maxims Encoded meaning of certain connectives/expressions (“but,” “even”) By logical form (connectives like “if…then,” “⊃”)
Part of truth-conditions? No (backgrounded) No (inferred) No (side meaning) Yes (conditional’s truth value)
Speaker commitment Speaker acts as though presupposition is already true Speaker suggests but need not be committed if implicature later canceled Committed to content plus the conventional side meaning Committed to the truth of the entire implication (ϕ → ψ)
Can it be canceled outright? Generally non-cancelable (must be “accommodated” or the utterance crashes: “Oops, sorry—John doesn’t actually have a sister”) Yes (“The soup was warm—but I don’t mean it was bad”) Normally non-cancelable without contradiction (“#She’s poor but happy—no contrast intended”) Not cancelable without changing the proposition; you can reject it, but you cannot assert it and deny it simultaneously
Projection under negation / questions? Projects: “Did you quit?” presupposes you used to smoke Does not project: negating or questioning typically removes the implicature Survives negation: “She isn’t poor but happy” still conveys contrast Negated wholesale: “¬(ϕ → ψ)” is simply a different proposition
Relies on shared context? Yes—must be accepted or accommodated Yes—derived from expectations about relevance, quantity, etc. Minimal—relatively context-insensitive once the trigger is used No—purely logical truth values
Test for presence Survival test (“Stop smoking” vs. “Did you stop smoking?”) Cancelability & calculability tests Lexical replacement (“but” → “and”) removes the contrast Truth-table and entailment tests
Typical example “John’s dog is asleep.” ⇒ presupposes John has a dog. “It’s getting late.” (in reply to an invitation) ⇒ implies “I should go.” “She is small but strong.” ⇒ conventional contrast. “If the alarm rings, there is a fire.” ⇒ logical implication.

3. How they interact (quick notes)

  1. Logical form vs. pragmatic enrichment

    • You can embed a presupposition trigger inside a material conditional:

      “If the king of France is bald, we need new barbers.” The conditional truth value is classical, but the presupposition (“France currently has a king”) still has to be accommodated—or else the sentence feels odd.

  2. Implicature on top of implication

    • Saying “If you finish your thesis, I’ll buy you dinner” often implicates the speaker believes the antecedent is uncertain (Quantity/Relevance). The implicature disappears if you add “…and even if you don’t, I’ll still buy you dinner.”
  3. Presupposition vs. logical entailment

    • “John regrets leaving” presupposes “John left,” but it does not logically entail “John left” in the truth-functional sense (because under negation—“John doesn’t regret leaving”—the truth of “John left” is still taken for granted).

4. Quick mnemonic

  • Presupposition: “Assumed in advance.” It must already be in the common ground (or swiftly get there).
  • Implicature: “Implied by pragmatics.” It’s worked out by cooperative reasoning and can be withdrawn.
  • Implication (logical): “If-then formal link.” It’s built into the proposition’s structure and evaluated truth-functionally, independent of conversational cooperation.

Keeping these distinctions straight helps disentangle what a sentence asserts, what it takes for granted, and what it merely suggests.

There is a continuation in this other post.

Thursday, May 1, 2025

Denumerability of Subsets

Given an enumeration \( a_1, a_2, \ldots, a_n, \ldots \) of a denumerable set \( A \), and an enumeration \( b_1, b_2, \ldots, b_n, \ldots \) of a denumerable set \( B \), we can enumerate \( A \cup B \) in the order \( a_1, b_1, a_2, b_2, \ldots, a_n, b_n, \ldots \). Write a more general version of this statement for the union of arbitrarily many denumerable sets.

More generally, one shows:

Theorem.
If \(\{A_m\}_{m\in\mathbb N}\) is a countable family of countable (denumerable) sets, then
\[ \bigcup_{m=1}^\infty A_m \] is itself countable.

Proof sketch (by explicit enumeration).

  1. For each \(m\in\mathbb N\), choose an enumeration

\[ A_m = \{\,a_{m,1},\,a_{m,2},\,a_{m,3},\dots\}. \]

  1. Recall that \(\mathbb N\times\mathbb N\) is countable. Fix any bijection

\[ \pi\colon \mathbb N \;\longrightarrow\;\mathbb N\times\mathbb N, \quad \pi(k) = (m(k),\,n(k)). \]

  1. Define a new sequence \((c_k)_{k=1}^\infty\) by

\[ c_k \;=\; a_{\,m(k),\,n(k)}. \]

  1. Then \(\{c_1,c_2,c_3,\dots\}\) lists every element of \(\bigcup_{m}A_m\)

(possibly with repetitions), showing that \(\bigcup_m A_m\) is countable.


Equivalently, if you prefer a “diagonal” style ordering without naming a particular pairing function, you can say:

  1. List all pairs \((m,n)\in\mathbb N\times\mathbb N\) in a single sequence, for example by moving along the diagonals

\[ (1,1),\;(1,2),(2,1),\;(1,3),(2,2),(3,1),\;\dots \]

  1. Whenever \((m,n)\) appears in that list, write down the \(n\)-th element of \(A_m\), namely \(a_{m,n}\).

Because every pair \((m,n)\) will eventually appear, every element of every \(A_m\) will be listed. Hence a countable union of countable sets is countable.

The set of all infinite subsets of \( \mathbb{N} \) cannot be denumerable; show this by showing that the set of all finite subsets of \( \mathbb{N} \) is denumerable, and then applying the above theorem.

First, partition the collection of all finite subsets of \(\mathbb{N}\) by their cardinality. For each \(k\in\mathbb{N}\), let \[ \mathcal F_k = \bigl\{\,F\subseteq\mathbb{N}:\lvert F\rvert = k\bigr\} \] be the family of all \(k\)-element subsets of \(\mathbb{N}\). Then:

  1. Each \(\mathcal F_k\) is denumerable.
    An easy way to see this is to observe that any \(k\)-element subset can be written uniquely as an increasing \(k\)-tuple \[ (n_1,n_2,\dots,n_k) \quad\text{with}\quad n_1<n_2<\cdots<n_k,\quad n_i\in\mathbb{N}. \] The set of all such increasing \(k\)-tuples is a subset of \(\mathbb{N}^k\), and \(\mathbb{N}^k\) is denumerable (e.g.\ by the diagonal‐pairing argument). Hence each \(\mathcal F_k\) is denumerable.

  2. The set of all finite subsets is a countable union of the \(\mathcal F_k\).
    \[ \mathcal F \;=\; \bigl\{\,F\subseteq\mathbb{N}:\lvert F\rvert<\infty\bigr\} = \bigcup_{k=0}^\infty \mathcal F_k. \] By the theorem you just proved, a countable union of denumerable sets is denumerable. Therefore \(\mathcal F\) is denumerable.


Now let \[ \mathcal I = \bigl\{\,X\subseteq\mathbb{N}:\lvert X\rvert=\infty\bigr\} \] be the collection of infinite subsets of \(\mathbb{N}\). Notice that every subset of \(\mathbb{N}\) is either finite or infinite, so \[ \mathcal P(\mathbb{N}) \;=\; \mathcal F \;\cup\;\mathcal I. \] Suppose, for the sake of contradiction, that \(\mathcal I\) were denumerable. Then \(\mathcal F\) is denumerable (as shown above), so by the union theorem again, \[ \mathcal P(\mathbb{N}) = \mathcal F \cup \mathcal I \] would be a union of two denumerable sets, hence itself denumerable. But this contradicts Cantor’s theorem that \(\mathcal P(\mathbb{N})\) is uncountable. Therefore our assumption was false, and \(\mathcal I\) cannot be denumerable. In other words, the family of all infinite subsets of \(\mathbb{N}\) is uncountable.