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Use a fresh existential witness $v$. Within the assumption of the forbidden universal quantification, eliminate the original existential quantification to obtain logical falsity:The witness $v$ appears neither in the exported conclusion $\perp$ at line 6 nor in the other undischarged assumptions. The existential quantifier elimination rule is therefore valid, and

Use negation introduction on the unwanted existential quantification, and existential quantifier elimination rule with a fresh witness $a$:The witness name occurs neither in the surrounding assumptions nor in the conclusion $\perp$ of its subproof. This satisfies the eigenvariable condition; it is not an arbitrarily chosen fixed element assumed to witness the existential quantification. The result is $\neg \exists xP(x)$.

The printed expression has one unmatched closing parenthesis. Remove that final surplus parenthesis and read the first disjunct as $\exists xP(x)$; the other disjunct has outer universal quantification over the displayed implication. To distinguish the two independent bound uses of $x$, rename the first to $u$.

Push the outer negation through the logical disjunction and through the quantifiers. Negating the implication preserves its existential antecedent and negates its consequent. Applying De Morgan's laws and the quantifier negation rules gives the negation normal formEach negation is now directly before an atomic formula. No bound variable has been captured, and no original existential quantification in the antecedent has accidentally been negated twice.

A primitive positive formula is generated from atomic formulas using only logical conjunction and existential quantification. It can be put in the form $\exists \bar{y}{\bigwedge }_j{\theta }_j(\bar{x},\bar{y})$ with each ${\theta }_j$ atomic. The exam terminology tame formula refers to this fragment. Its reduced product transfer needs only filter intersection and upward closure, together with coordinatewise choices of witnesses.

A quantifier binds a free variable in a first-order formula and specifies how its truth depends on elements of a first-order structure: universal quantification $\forall$ requires every element, while existential quantification $\exists$ requires at least one.

Second-order logic extends first-order logic by allowing existential quantification and universal quantification over second-order relation variables, as well as over individual objects. In the relational presentation used here, an individual variable $x$ ranges over a domain $D$, while a second-order relation variable $R$ of arity $k$ denotes a subset of $D^k$. Unary second-order relation variables denote subsets of $D$; binary ones denote binary relations. Presentations with function variables can instead describe their graphs by second-order relation variables, with first-order formulas requiring a unique output for every input.

For a graph with vertices $D$ and fixed graph adjacency $E$, the first-order sentence $\forall x\exists yE(x,y)$ says that every vertex has a neighbour. It selects individual vertices while $E$ retains its given interpretation. By contrast,selects an entire subset $P$ and says that it is a nonempty independent set. Here $P$ is a unary second-order relation variable, rather than a symbol in the input first-order signature. This particular property also has a simple first-order sentence on simple graphs; the example illustrates the change in what can be selected, rather than proving a separation in expressive power.

The usefulness of second-order logic is that one can describe a whole proposed solution, such as a graph coloring, and then impose local constraints on it. Syntax alone does not specify which proposed relations are available: full semantics for second-order logic permits all external subsets of $D^k$, whereas Henkin semantics supplies designated ranges. The second-order encoding of three-colorability makes this distinction concrete.

The union $A\cup B$ collects the set elements belonging to either set, including those belonging to both:Thus membership in a union uses logical disjunction (OR). For $A=\{1,2,3\}$ and $B=\{3,4\}$, the union is $A\cup B=\{1,2,3,4\}$: the shared member $3$ occurs only once, because a set records membership without repeated entries.

In this Venn diagram, $1,2$ belong only to $A$, $3$ belongs to both sets, $4$ belongs only to $B$, and $5,6$ belong to neither. The surrounding universal set is $U=\{1,2,3,4,5,6\}$. Numbers are placed by membership; circle areas do not represent cardinalities. Each panel shades the region selected by its operation.

For a database illustration, let $A$ collect records matching one condition and $B$ collect records matching another. Their union selects records matching at least one condition, with a record satisfying both still appearing once. This interpretation turns an abstract operation into a reusable rule for combining selections. For an authored treatment of union, intersection, set difference, and complement, see Lehman, Leighton and Meyer, Mathematics for Computer Science (2015), Definition 4.1.1, pp. 82–83.

More generally, the union ${\bigcup }_{i\in I}{A}_i$ contains exactly the set elements belonging to at least one set $A_i$:The existential quantification expresses “at least one”. With no indexing members, the union is the empty set. Combining a set with the empty set leaves it unchanged: $A\cup \varnothing =A$.

Tuple relational calculus describes query answers by predicates over database tuples rather than a sequence of storage operations. A set-builder expression $\{t:P(t)\}$ returns tuples satisfying a formula built from relation membership, attribute comparisons, logical connectives and quantifiers. For example,selects employee tuples whose salary exceeds100. A join can be expressed using existentially quantified tuples from two relations and equality of their shared identifying attribute.

The formula needs a safety restriction. Over an unrestricted infinite value universe, a query such as $\{t:\neg \mathrm{Employee}(t)\}$ can demand infinitely many absent tuples. Safe tuple relational calculus restricts results to information grounded in the input and explicit constants. Under the usual finite-relation assumptions, its expressive power agrees with relational algebra. This gives a declarative query language while leaving the database system free to choose an efficient query plan.

A sequence of functions $f_n:X\to Y$ converges uniformly to $f:X\to Y$, where $Y$ is a metric space with metric $d$, precisely whenFor real numbers, use the absolute value $d(u,v)=|u-v|$. Compare the universal quantification and existential quantification:The decisive change is whether $N$ can depend on $x$. Uniform convergence implies pointwise convergence.^[ref]

For functions taking values in real numbers on a nonempty domain of a function, the supremum error is $E_n={\sup }_{x\in X}|f_n(x)-f(x)|$, allowing $+\infty$. Then uniform convergence is equivalent to $E_n\to 0$. The supremum need not be a maximum: pointwise strict bounds below $\epsilon$ only give $E_n\le \epsilon$, so use tolerance $\epsilon /2$ when proving this equivalence. This single error bound controls every possible input, including one chosen after seeing the index.

For $f_n(x)=x^n$ and its pointwise limit on $[0,1]$, the error is zero at one but approaches one as $x\to 1$ from below. Consequentlyfor every positive integer $n$. This supremum is never attained: below one the error is $x^n<1$, and at one it is zero. Deleting the endpoint leaves the same supremum error on $[0,1)$. To disprove uniform convergence directly, choose $\epsilon =1/2$: for any proposed $N$, take $n=N$ and $x=2^{-1/N}<1$. The error equals $1/2$, violating the strict tolerance. Thus a moving input defeats every proposed common threshold.

On the smaller interval $[0,a]$, for a fixed $0\le a<1$, the pointwise limit is zero andFor $0<a<1$ and $0<\epsilon <1$, the exact first successful common threshold isThe logarithm and floor function turn an infinite approximation statement into a finite stopping rule. At $a=0.8$, $0.8^{20}=0.01152921504606846976$ fails tolerance $0.01$, while $0.8^{21}=0.009223372036854775808$ succeeds. At $a=1/2$ and $\epsilon =1/8$, the ratio is exactly three: $N=4$ is necessary because equality is insufficient. For $a=0$, every error is zero. The domain of a function determines whether one threshold can certify every input.

For another explicit stopping rule, let $h_n(x)=x/(n+1)$ on $[0,1]$. Its uniform limit is zero and its supremum error is $1/(n+1)$. For every $\epsilon >0$, $N=\max \{1,\lceil 1/\epsilon \rceil \}$ works, using the ceiling function. Unlike $x^n$ on $[0,1]$, it controls even the endpoint.

The practical beauty of uniform convergence is that one quantitative bound can survive further operations: the uniform limit theorem preserves continuity, and uniform convergence preserves integrals on a compact interval controls accumulated error. Differentiation needs additional hypotheses, such as uniform derivative convergence with an anchored value. These results distinguish an approximation that looks accurate at selected inputs from one that justifies replacing a whole function in a calculation.

Universal quantification forms $\forall x\phi (x,\mathbf{y})$. In a first-order structure, it is true at an assignment to $\mathbf{y}$ when every domain element assigned to $x$ makes the instance true. Its introduction rule requires that the generalized variable not occur freely in undischarged assumptions.

Over the natural numbers, reversing the preceding existential quantification and universal quantification gives a true first-order sentence:Choose $m=n+1$. The computed witnesses for $n=0,1,2,3$ are $m=1,2,3,4$. The choice may depend on the previously given $n$. The formula states that every natural number has a larger natural number; the reversed formula demands one larger than all of them.

The diagram displays a finite window, while the rules $m=n+1$ and $n=m$ settle the two statements for every natural number. This dependence is why quantifier order matters in precise specifications. Choosing one $m$ that works for every $n$ would also provide a witness for each $n$; the reverse inference fails here.

To introduce universal quantification in natural deduction, reason with an arbitrary input, without assuming a special property of it. The construction $m=n+1$ does exactly this and also gives a proof in intuitionistic first-order logic. The introduction and elimination rules for both quantifiers are developed in Frank Pfenning, First-Order Logic and Type Theory, section 4.1.