A cavity in a conductor is a void surrounded by an electrical conductor; completely enclosed is the essential qualification in exact electrostatic shielding arguments. At electrostatic equilibrium, the surrounding connected metal is an equipotential. An empty closed cavity in a conductor has constant electric potential and zero electric field, even if the metal is isolated and charged. An occupied closed cavity in a conductor generally has a nonzero electric field; its inner-wall total electric charge cancels the total electric charge of the internal sources. These are distinct statements, illustrated by Faraday's ice pail experiment.^[ref]

A messenger names an excluded candidate while concealing the uniquely selected one. The conditional probability depends on the naming rule. With three equally likely selected candidates and a messenger forbidden to name the observing candidate A, let $s$ be the probability of naming B when A is selected. When C is selected the messenger must name B, and when B is selected cannot name B. Thus the probability that A is not selected, conditional on B being named, is $1/(1+s)$. Unbiased naming has $s=1/2$ and leaves this probability $2/3$; bias toward B gives $1/2$, and bias toward C gives one. Conditioning on the name is different from conditioning only on B's exclusion.

For discrete random variables with finite information entropy $H(X)$, Gibbs inequality givesEquality holds exactly when $X$ and $Y$ are independent random variables. Finiteness matters: if $A$ has infinite information entropy and is independent of a fair bit $B$, then $X=(A,B)$ and $Y=B$ are dependent but $H(X)=H(X\mid Y)=+\infty$.

// Target: probability-and-statistics.bigb

Let a differentiable concave function on all of ${\mathbb{R}}^d$ be strictly increasing in each coordinate separately. Every coordinate derivative is strictly positive. Indeed it is nonnegative by monotonicity and nonincreasing along its coordinate by concavity; a zero value would force zero derivative on the remaining positive ray and hence a constant segment. The global-domain assumption matters at endpoints of restricted domains.

For $0<c<1$ on the probability simplex, $x_i^c\ge x_i$ gives the lower bound, with equality exactly at its vertices. Strict concavity and the Jensen inequality give the upper bound, with equality exactly at the uniform point $x_i=1/n$. Thus the boundary can minimize a strictly concave objective, while its unique maximum lies at the symmetric interior point; a Lagrange-multiplier calculation alone would miss the boundary minimum.

Assume independent physical-error probability $p$, correction of zero or one errors, and failure on every block with at least two errors. The failure map is $f(p)=1-(1-p{)}^5-5p(1-p{)}^4$. Factoring $f(p)-p$ gives $p(1-p)(4p^3-11p^2+9p-1)$. The unique interior threshold is the root of $4p^3-11p^2+9p-1=0$. Below it concatenation decreases the failure probability toward zero. Two levels give $f(f(p))=1000p^4+O(p^5)$, compared with $10p^2+O(p^3)$ at one level.

// Target: quantum-information-theory.bigb

For two noncoplanar triangles, first reject vertices strictly on one side of the other triangle's plane. Otherwise intersect each triangle with the other plane and compare the resulting intervals along the common line. Coplanar triangles require a planar overlap test; zero-area triangles require separate line segment or point tests.

// Target: analysis.bigb

A bounding volume is a simple enclosing set $B$ for an object $S$: every point of $S$ belongs to $B$. Its purpose is to make a cheap rejection possible before examining the object itself. For example, an axis-aligned bounding box can enclose a triangle using just its smallest and largest coordinates.

The guarantee is one-sided. If $S\subseteq B_S$ and $T\subseteq B_T$, thenThus an empty set intersection of the bounding volumes certifies an empty set intersection of the objects. An overlap of the bounding volumes is necessary for actual intersection, but does not prove it. This is a conservative enclosure test: rejection is conclusive, acceptance means further examination is needed.

Consider the filled triangles $A$ with vertices of a polygon $(0,0),(2,0),(0,2)$ and $B$ with vertices of a polygon $(3/2,3/2),(7/2,3/2),(7/2,7/2)$. Their axis-aligned bounding boxes overlap in $[3/2,2]\times [3/2,2]$. Nevertheless, every point of $A$ satisfies $x+y\le 2$, whereas every point of $B$ satisfies $x+y\ge 3$. The two triangles are disjoint. The shaded square in the bounding volume hierarchy illustration makes this false candidate visible.

For collision detection, a broad phase produces possible object pairs from bounding volume tests; a narrow phase examines the actual shapes, for example with a triangle-triangle intersection algorithm. Scott Le Grand's Chapter 32. Broad-Phase Collision Detection with CUDA explains this separation in its opening paragraphs. Whether touching counts as a collision must be specified by the application.

An enclosure must cover the entire object being queried. A box fitted to samples of a parametric curve only guarantees enclosure of the sampled points, unless a separate bound covers the whole parametric curve. For a Bézier curve, the convex hull of its control points provides such a bound on its defining parameter interval. In floating-point arithmetic, outward rounding can preserve certified coordinate bounds; an inward roundoff error can invalidate a rejection. Bounds must also be updated when objects move.

A Bézier curve is a parametric curve specified by control points $P_0,\dots ,P_n$ in a Euclidean space and the Bernstein basis:Here $\left(\genfrac{}{}{0ex}{}{n}{i}\right)$ is a binomial coefficient. Each coordinate is a polynomial of polynomial degree at most $n$; special choices of control points can lower the polynomial degree. The ordered control points form the control polygon. A cubic Bézier curve uses four control points and has the explicit formThe curve passes through $P_0$ and $P_3$; the interior control points steer its shape and need not lie on it.^[ref]

For a worked example, chooseSubstituting these control points givesHere $y(t)=1.5-6(t-1/2{)}^2\le 1.5$, so neither interior control point, both at height $2$, lies on the curve. De Casteljau's algorithm computes the same point using only successive linear interpolations.

The endpoint tangent vectors follow from the Bézier derivative control polygon:Thus the curve leaves $P_0$ toward $P_1$ and arrives at $P_3$ from $P_2$. These nonzero derivatives determine the endpoint tangent lines; if an endpoint derivative vanishes, that formula alone does not determine its tangent direction. Endpoint handles are useful when arranging a parametric first-derivative join of Bézier curves.

The parameter $t$ is usually not arc length. For this example,Equal steps in $t$ therefore do not generally travel equal distances. Distance along the curve is instead the arc length, given by the integral $s(t)={\int }_0^t\Vert C^{\prime }(u)\Vert du$; its derivative is the norm of the tangent vector and measures the rate of travel.

Two geometric facts make Bézier curves especially convenient for design. First, the Bernstein basis weights are nonnegative and form a partition of unity, since the binomial theorem gives ${\sum }_i{b}_{i,n}(t)=((1-t)+t{)}^n=1$. Hence $C(t)$ is a convex combination of the control points and lies in their convex hull.^[ref] The convex hull is the smallest convex set containing the control points, not their axis-aligned bounding box. Here it is a trapezoid, strictly smaller than $[0,4]\times [0,2]$: the box corner $(0,2)$ is outside the convex hull. The curve need not fill or touch every edge of either enclosure. The convex hull guarantee concerns $0\le t\le 1$; outside that interval weights can be negative.

Second, applying an affine map to every control point gives exactly the same result as applying it to the entire curve. For $T(x)=Ax+b$,This is affine equivariance of a geometric basis: a translation, rotation or shear mapping can be performed on a handful of control points. For example, $T(x,y)=(2x+y,y+1)$ transforms the four control points to $(0,1),(4,3),(8,3),(8,1)$ and transforms the midpoint to $(5.5,2.5)$, also obtained from the same weights $(1,3,3,1)/8$.

These editable handles explain the usefulness of Bézier curves in computer aided geometric design, vector graphics paths and outline fonts: a small collection of points controls an entire smooth stroke, and resizing the design preserves its mathematical shape. Cambridge's computer-graphics notes describe their use in graphic design, typography and PostScript outlines. The industrial context includes Pierre Bézier's work at Renault and Paul de Casteljau's work at Citroën.^[ref]

// Target: analysis.bigb

A subdivision surface is a limit shape defined by repeated refinement of a control net using a specified subdivision mask. Each step changes the mesh connectivity and computes new control points from nearby old ones. When the scheme converges, progressively refined polygonal meshes approach the limit; the finite mesh at any chosen level is generally an approximation to that limit.

In Loop subdivision, each triangular face splits into four smaller triangles, and both old-vertex positions and new edge-vertex positions are weighted averages. These local rules allow a mesh with many different degrees of vertices to represent a smooth shape without constructing one global rectangular parameter grid. On a regular patch, a known box spline describes the limit. Near an extraordinary subdivision vertex, the subdivision matrix and characteristic map of a subdivision surface govern convergence and tangent behavior.

Rendering a refined mesh and querying the exact limit are different tasks. Subdivision surface interrogation evaluates a limit point, derivatives and a normal vector where they exist, and needs bounds that enclose the limit. Additional refinement makes an approximation finer, but alone does not prove a specific geometric error bound or guarantee Gaussian curvature exists at every extraordinary point.^[ref]

// Target: analysis.bigb

A subdivision curve is the limit of repeated refinement of a control polygon, when that limit exists. Each finite stage still consists of line segments; the limiting curve can have a continuous derivative. Linear stationary rules, which use the same fixed weighted averages at every stage, can be studied through their subdivision masks and often represent splines. An interpolation rule retains the old control points, as in binary linear interpolatory subdivision; an approximating rule such as Chaikin subdivision generally moves away from them. Increasing the number of control points alone does not prove convergence or smoothness.

// Target: analysis.bigb

If $g$ is gravitational acceleration and $a$ is the camera's acceleration, the locally felt upward vector is $U=a-g$. Choose the travel unit vector $X$, set $W=U-(U\cdot X)X$, and, when $W\ne 0$, take $Z=W/\Vert W\Vert$ and $Y=Z\times X$. These form an orthonormal basis with $U$ in the $XZ$ plane. If $U$ is parallel to $X$, the condition leaves the roll angle free even when $U\ne 0$; transport a previous transverse axis or choose a well-conditioned coordinate axis and project it into $X^{\perp }$.

A control point is an editable geometric handle used to specify a curve or surface through a basis or refinement rule. The handle is part of the representation; it need not be a point on the resulting curve. In a Bézier curve, the control points are the geometric coefficients of the Bernstein basis. Joining them in order gives a control polygon, which makes the handles easy to manipulate without drawing the curve as a chain of line segments.^[ref]

For example, use the cubic Bézier curve with control pointsIts Bernstein basis representation givesThe greatest height is $1.5$, so neither interior control point, both at height $2$, lies on this curve. The endpoint control points do lie on it: $C(0)=P_0$ and $C(1)=P_3$. This separates interpolation, which requires passage through prescribed data, from an approximating representation, which uses handles to shape a curve without requiring passage through every handle. If measured points must be interpolated, choose control points to satisfy those constraints; simply using the measured points as the handles of a Bézier curve does not enforce interpolation.

Now move only $P_1$ by the vector $h=(0,1)$, giving ${\tilde{P}}_1=(1,3)$. With the Bernstein basis and parameter $t$ unchanged, the new curve isAt $t=1/2$ this gives $\tilde{C}(1/2)=(2,15/8)$, a displacement $(0,3/8)$ despite moving the handle by a full unit. Both endpoints stay fixed because the coefficient $3t(1-t{)}^2$ vanishes there. Thus dragging a control point shapes a whole range of the curve; it does not generally drag an arbitrary selected point on the curve by the same vector. The control-point influence on a curve tells us exactly how much each parameter value moves.

A basis with the displayed identities reproduces affine functions from their values at the precision sites ${\xi }_j$. For two such bases, the tensor-product precision sites are $({\xi }_j,{\eta }_k)$: summation independently recovers $u$ and $v$. Affine images of this site grid therefore produce exact planar parameterizations, independent of the chosen control-grid representation.

For overlapping solid bodies, the penetration depth is the smallest translation needed to remove interior overlap, with orientation fixed. It is distance from the origin to the complement of the translational collision region. Define signed separation as positive ordinary separation for disjoint bodies, zero for contact, and $-\delta$ for interior overlap. Translation-space branch and bound can bracket it using certified intersection/containment queries. This convention handles crossing and complete containment; merely negating unsigned boundary distance does not.

A geometry representation with scalar partition of unity basis functions commutes with every affine map of its control points. For $T(x)=Ax+b$, linearity moves $A$ through the sum and the sum-to-one identity supplies the translation $b$. Positivity is needed for a convex hull property, but not for this equivariance. The same argument applies to linear subdivision matrices whose rows sum to one. General projective transformations require homogeneous control points and rational dehomogenization rather than transforming Euclidean controls with unchanged weights.

For a partial function $g(\mathbf{x},y)$, unbounded minimization definesas the least $y$ at which $g$ is zero, provided the preceding required computations are defined. If no such $y$ is found, $f(\mathbf{x})$ is undefined. Closing the primitive recursive functions under this operation gives the partial recursive functions.

A universal partial computable function simulates the program with index $e$ on input $\mathbf{x}$, reproducing both its output and its divergence. Decode the finite program and repeatedly apply its transition rule. This gives a partial computable function of the code and inputs. There can be no total computable evaluator whose rows are precisely all total computable functions, because $n\mapsto U(n,n)+1$ would be a missing diagonal row.

Isentropic compressibility is the compressibility at fixed entropy, particle number and composition. The conventional name adiabatic compressibility refers to a reversible adiabatic process; an irreversible adiabatic compression need not keep entropy fixed. For an ideal gas, it is ${\kappa }_S=1/(\gamma P)$, where $\gamma =C_P/C_V$ is the heat capacity ratio. It describes a steeper local pressure response than isothermal compressibility when $\gamma >1$.

Isothermal compressibility is the compressibility measured at fixed temperature, particle number and composition. A slowly compressed sealed piston in contact with a thermal reservoir can approach this constraint. For an ideal gas, differentiating $V=N{k}_{B}T/P$ at fixed temperature gives $(\partial V/\partial P{)}_{T,N}=-V/P$, hence ${\kappa }_T=1/P$. A small pressure increase $\Delta P$ therefore gives $\Delta V/V\simeq -\Delta P/P$. For a finite change, use the whole isothermal process curve $V_2/V_1=P_1/P_2$ instead.

Take one mole of classical monatomic gas, with stipulated molar gas constant $R=8.314\mathrm{J}\mathrm{mo}{\mathrm{l}}^{-1}{\mathrm{K}}^{-1}$, initially at $T_0=300\mathrm{K}$ and $P_0=100\mathrm{kPa}$. The initial volume is $V_0=nR{T}_0/P_0=0.024942{\mathrm{m}}^3$. Compressing to $V_0/2$ givesThe isothermal compression releases heat to the thermal reservoir; the reversible adiabatic compression retains the input work as internal energy. Reversing either curve gives the corresponding expansion, with adiabatic expansion cooling the gas.

The equilibrium curvature of Gibbs free energy provides another measurement of isothermal compressibility:This follows from $V=(\partial G/\partial P{)}_{T,N}$, with $G$ denoting Gibbs free energy. Within a stable single thermodynamic phase, increasing pressure reduces volume and makes this curvature negative. At phase coexistence, changing the phase fraction can change the volume at constant temperature and pressure; the usual finite single-phase isothermal compressibility then need not exist. Near a critical point, it can diverge. ^[ref]