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Parallax localization of a virtual image matches the apparent depth of a virtual image to a directly viewed physical marker. A sideways change of viewpoint changes their relative apparent positions if their depths differ. Adjust the marker until that relative motion vanishes, and use its coordinate to estimate image position. The marker must be viewed directly; viewing it through the same optical lens compares two images rather than an image and a known physical depth. ^[ref].

For two nearly aligned points at distances $L_i,L_m$ from an eye displaced sideways by $\delta$, the small-angle approximation gives a change of relative viewing angle proportional to $\delta (1/L_i-1/L_m)$. It vanishes when $L_i=L_m$, and changes sign across that match. When both distances are large, the difference is small, explaining the poor depth precision near the thin lens condition $u=f$.

A minimum vertex cover is a vertex cover of smallest cardinality among all vertex covers of the same finite simple graph. The optimum size is the vertex cover number. By the complement of a set correspondence, a minimum vertex cover leaves a largest possible independent set unselected.

Minimum size is stronger than merely being unable to remove a selected vertex. In the star graph $K_{1,3}$, the three leaves form a vertex cover from which no vertex can be removed: each leaf covers its own edge. Nevertheless, the centre alone is a minimum vertex cover of size one. Such an inclusion-minimal vertex cover can therefore be much larger than a minimum vertex cover.

The dimension of a vector space is the cardinality of any of its bases. In finite dimension this counts the scalars needed to specify an arbitrary vector once an ordered basis is chosen. The exchange argument behind the basis extension theorem shows that any two finite bases have the same size: an independent family cannot be larger than a spanning family.

Thus ${\mathbb{R}}^2$ has dimension two even with an oblique basis, $\mathbb{R}[x{]}_{\le 2}$ has dimension three, and the homogeneous plane in vector subspace has dimension two. This count concerns independent freedom, so an embedded plane remains two-dimensional while sitting in a three-dimensional ambient vector space. Dimension also depends on the field, as the two interpretations of $\mathbb{C}$ in vector space illustrate.

Vector addition is the vector space operation combining two vectors into their sum. It is commutative and associative, has the zero vector as identity, and distributes with scalar multiplication as specified by the vector space axioms. Coordinatewise addition describes it after choosing a basis.

The zero vector of a vector space is its additive identity: $v+0=0+v=v$ for every vector $v$. It is unique, because if $0^{\prime }$ is another such identity then $0=0+0^{\prime }=0^{\prime }$. Distributivity gives $\alpha 0=\alpha (0+0)=\alpha 0+\alpha 0$, so $\alpha 0=0$ after cancellation. In a coordinate vector space it has every coordinate zero.

A sequence space is a vector space of scalar sequences, closed under coordinatewise addition and scalar multiplication. Examples include the l-p sequence space, the l-infinity sequence space and spaces of convergent sequences.

Addition makes $V$ an Abelian group: for all $u,v,w\in V$, $(u+v)+w=u+(v+w)$ and $u+v=v+u$, there is a zero vector $0$, and every $v$ has an additive inverse $-v$. Scalar multiplication satisfies, for $a,b\in \mathbb{F}$,Both operations take values in $V$. These requirements make manipulating linear combinations independent of how sums are grouped or expanded.

For positive $r,k$, let $W(r,k)$ bound the interval needed to force a monochromatic length-$k$ arithmetic progression in every finite colouring using at most $r$ colours. The colour-focusing proof of Van der Waerden theorem inducts on $k$, simultaneously for every number of colours, and then creates progressions of distinct colours with a common next point.

Assume $W(r,k)$ exists for every $r$. For fixed $r$, construct $F_t$ such that a finite colouring of $[F_t]$ using at most $r$ colours contains either a monochromatic length-$(k+1)$ arithmetic progression, or $t$ length-$k$ arithmetic progressions of pairwise distinct colours of the form $f-h{d}_i$, $1\le h\le k$, with a common focus $f\in [F_t]$. Take $F_1=2W(r,k)$: a length-$k$ arithmetic progression in its first half has its next point inside the whole interval. For $k=1$, give a singleton step $1$.

Given $N=F_t$, put $M=W(r^N,k)$ and $F_{t+1}=2NM$. Regard the first $M$ length-$N$ blocks as coloured by their full colour profiles. Some block indices $j_0,j_0+D,\dots ,j_0+(k-1)D$ share a profile. If there is no monochromatic length-$(k+1)$ arithmetic progression, this profile supplies $t$ focused progressions with focus position $f$, steps $d_i$, and distinct colours $q_i$. The colour $q$ of position $f$ differs from every $q_i$. In the later block $j_{\ast }=j_0+kD\le 2M$, put $F=N(j_{\ast }-1)+f$. For each $i$, the points $F-h(ND+d_i)$ have colour $q_i$: they lie in the selected block $j_{\ast }-hD$ at position $f-h{d}_i$. The points $F-hND$ have colour $q$. Thus $t+1$ distinct colours focus at $F$. For $t=r$, its own colour completes one of the $r$ focused progressions. The base case $W(r,1)=1$ completes the mathematical induction.

For a sphere of radius $a$ at separation $d\ll a$ from a half-space, pairwise integration of the attractive $r^{-6}$ molecular potential gives $F\simeq Aa/(6d^2)$. Equivalently, integrate the planar disjoining pressure magnitude $A/(6\pi h^3)$ over the local gap $h=d+r^2/(2a)$. Across a third medium the effective Hamaker constant can instead correspond to repulsion; state its sign convention explicitly.

At a point where $\varphi (b)$ is finite, this is an affine plane supporting the epigraph from below: $\varphi (u)\ge \varphi (b)+{\lambda }^T(u-b)$ for every $u$. A nonzero coefficient of the vertical coordinate permits normalization to the displayed graph form. For a constrained value function, the slope is exactly an attained multiplier in the Strong Lagrangian property. When the value function is differentiable at an interior point of its finite domain, a supporting slope equals its gradient: apply the supporting inequality along both positive and negative multiples of each coordinate direction and take limits.

Let annual losses be $L={\sum }_{i=1}^N{X}_i$ where $N\sim \mathrm{Poisson}(\lambda )$ is independent of an independent identically distributed sequence of nonnegative loss given default amounts of mean $\mu$ and variance ${\sigma }^2$. Conditional expectation and variance give $\mathbb{E}L=\lambda \mu$ and $\mathrm{Var}L=\lambda ({\sigma }^2+{\mu }^2)$. A many-event normal approximation yields the displayed quantile; the actual loss distribution has a zero atom and depends on the entire severity law. For rare events or extreme tails, the moment-based normal approximation need not be accurate.

Electron pair geometry describes the arrangement of all electron domains in the VSEPR model, including lone pairs. Molecular geometry instead describes the arrangement of chemical atoms. Four domains have a tetrahedral ideal arrangement, but keeping only the bonded neighbours gives the different molecular geometries of methane, Ammonia and water.

An electron domain is a region of electron density counted around a central chemical atom in the VSEPR model. One lone pair counts as one domain; a bond to one neighbour counts as one domain even when its bond order is two or three. Domain count is therefore different from simply counting electron pairs.

In the two-component spinor convention with primed symmetric gauge curvature self-dual, an Abelian Maxwell field is anti-self-dual when its primed symmetric gauge curvature two-component spinor vanishes. For a locally exact field $F=dA$, the Bianchi identity gives $dF=0$; the duality relation then also gives $d\ast F=0$. Thus it obeys the source-free Maxwell equations. In complexified Lorentzian signature the Hodge star eigenvalues are $\pm i$; the two-component spinor condition specifies which component is meant without imposing a Euclidean eigenvalue on that signature.

// Destination: general-relativity.bigb

The expected utility hypothesis ranks uncertain outcomes by the expected value of a utility function. When these preference assumptions hold, expected utility maximization selects an admissible outcome with greatest expected utility.

Expected utility is the expected value of a utility function applied to a random outcome, using the decision-maker's probabilities. For a finite lottery, it is ${\sum }_j{p}_{j}u({\omega }_j)$. It is the expectation of utility, which generally differs from utility of the expected outcome.

For a risk-neutral bidder with fixed valuation $v$, winning indicator $I$ and possibly random payment $P$, quasilinear utility gives $vI-P$ and henceIf losing bidders pay nothing, this can be written $\mathrm{Pr}(I=1)\mathbb{E}[v-P\mid I=1]$ when the winning probability is positive. It cannot be used without that payment assumption: in an all-pay auction, a deterministic bid $b$ is paid even after losing, so the expectation is $v\mathrm{Pr}(I=1)-b$.

The additional utility function obtained per additional unit of consumption or portfolio wealth. For a differentiable utility function it is its derivative; for a concave utility function it is nonincreasing. In a complete-market allocation, equating marginal utility to a multiplier times the state-price density compares the utility benefit of a payoff with its financing cost. The concave supporting-tangent inequality makes this comparison a global optimality certificate rather than only a local first-order condition.

For increasing expected utility preferences, an investor is risk-averse when a sure mean is at least as desirable as the corresponding lottery. A concave utility function gives this preference by the Jensen inequality; a strictly concave utility gives strict preference for nondegenerate risks. Local risk aversion is measured by $-U^{\prime \prime }/U^{\prime }$ and its relative counterpart $-W{U}^{\prime \prime }/U^{\prime }$.

For increasing convex expected utility preferences, a decision-maker weakly prefers a lottery to its sure mean, provided the outcome and utility expectations are defined: the Jensen inequality gives $U(\mathbb{E}W)\le \mathbb{E}U(W)$. Strict preference for every nondegenerate lottery requires strict convexity over its relevant outcome range. An affine utility is convex too, but gives equality and risk neutrality.

For example, on nonnegative wealth, $U(w)=w^2$ assigns the equal-probability lottery $0$ or $2$ expected utility $2$, above the utility $1$ of its sure mean. Where $U^{\prime }>0$ and $U^{\prime \prime }>0$ at positive wealth, the relative risk aversion coefficient $-W{U}^{\prime \prime }/U^{\prime }$ is negative. This local curvature statement should be distinguished from strict preference when a utility has affine segments.

Start with a well-founded universe of sets over an infinite set $A$ of urelements. The full symmetric group of $A$ acts recursively on sets. Retain the objects all of whose descendants have a finite support in a permutation action. This class satisfies set-extensionality, foundation, infinity, pairing, union, separation, replacement and its internal power set axiom. A definable functional image has the finite union of the supports of its domain and parameters as a support; uniqueness makes the image invariant. The family of two-element subsets of $A$ has no choice function: a transposition of two atoms outside the function's finite support fixes their pair and moves either possible choice. For untyped membership with extensionality merely restricted to nonempty objects, atoms are also vacuous subsets; add all atoms to each internal power-set object.