An ideal straight line vortex in a two-dimensional incompressible flow has circulation $\Gamma$ and induces velocityIts stream function, with $\mathbf{u}=({\psi }_y,-{\psi }_x)$, is $\psi =-\Gamma \log |\mathbf{r}-{\mathbf{r}}_0|/(2\pi )$. Positive $\Gamma$ means counterclockwise circulation. In an ideal point-vortex description, the vortex position is advected by the regular velocity produced by the other vortices and the boundaries; its own singular velocity is excluded.

The evolution of a vorticity distribution together with the velocity field it induces. A transport equation advances vorticity; diagnostic inversion recovers velocity from it and the specified boundary conditions.

For density operators supported on mutually orthogonal subspaces, the eigenvalues of their mixture are $p_i{\lambda }_{ij}$, where ${\lambda }_{ij}$ are the eigenvalues of ${\rho }_i$. Substitution into the definition of Von Neumann entropy gives the displayed decomposition into Shannon entropy of the label and the mean entropy within each block. Zero eigenvalues use $0\log 0=0$.

For density operators in dimension $d$, let $\delta =\frac{1}{2}\Vert \rho -\sigma {\Vert }_1$. The positive and negative parts of their difference have the same trace $\delta$. Normalize them as ${\tau }_{+},{\tau }_{-}$ and write $(\rho +\delta {\tau }_{-})/(1+\delta )=(\sigma +\delta {\tau }_{+})/(1+\delta )$. Applying the two entropy bounds for a quantum mixture to this common state proves the displayed bound. The function $g(\delta )=(1+\delta )h_2(\delta /(1+\delta ))$ is increasing and concave, with $g(0)=0$, which also controls average entropy errors in pure-source compression.

For thresholds $U,V$, write ${\mu }_{\le U}(d)=\mu (d)1_{d\le U}$ and similarly for the other truncations. Vaughan's identity isIt decomposes a sum weighted by $\Lambda$ into short, Type I, and Type II bilinear sums.

The volume per unit mass; for a uniform substance it is the reciprocal of mass density. The Clapeyron equation pairs a difference of specific volumes with latent heat per unit mass.

The volume divided by the positive number of particles $N$. The Van der Waals equation uses this convention for $v$, whereas specific volume divides by mass and molar volume divides by the amount of substance. With the Avogadro constant $N_A$, the molar volume is $v_m=N_{A}v$.

The volume per amount of substance, with $n$ measured in moles. For a uniform pure substance, $v_m=M/\rho$, where $M$ is its molar mass and $\rho$ its mass density. The Clapeyron equation pairs a difference of molar volumes with latent heat per mole.

A spherical volume conductor model approximates a conducting body by a sphere, usually with uniform or radially stratified conductivity. It provides explicit forward models for electroencephalography and magnetoencephalography. Its symmetry makes a radial current dipole magnetically silent outside the sphere; departures from spherical symmetry need not retain that property.

For a homogeneous volume conductor with an insulating boundary $S$, the Geselowitz formula expresses the exterior magnetic field as the primary Biot-Savart law field minus a surface contribution from the induced electric potential. For a current dipole, ${\mathbf{B}}^p={\mu }_0\mathbf{Q}\times (\mathbf{r}-{\mathbf{r}}_0)/(4\pi |\mathbf{r}-{\mathbf{r}}_0{|}^3)$. On a spherical boundary, ${\mathbf{n}}^{\prime }$ is parallel to ${\mathbf{r}}^{\prime }$, so the surface term has zero radial component at the observation point.

The iteration $y_0=x$, $y_n=x-{\int }_0^x(x-s)y_{n-1}(s)/sds$ successively appends terms of the displayed series. Induction verifies the iterates, and factorial denominator bounds give convergence with derivatives on compact sets. Its limit is the power series solution of $x{y}^{\prime \prime }+y=0$, $y(0)=0$, $y^{\prime }(0)=1$.

A symmetric thin viscous sheet with shear-free faces has leading longitudinal velocity independent of thickness coordinate $z$, and incompressibility gives $w=-z{u}_x$. Young–Laplace equation and the Newtonian fluid stress tensor give ${\sigma }_{zz}=-p_a+\gamma h_{xx}/2$ and ${\sigma }_{xx}=-p_a+\gamma h_{xx}/2+4\mu u_x$. The effective longitudinal tension per unit transverse width is $\mathcal{T}=4\mu h{u}_x+\gamma h{h}_{xx}/2-\gamma h_x^2/4+2\gamma$. The last two terms include the horizontal projections of the two surface tension pulls. Force balance gives ${\mathcal{T}}_x=0$, yielding the displayed longitudinal equation; the face kinematic conditions give the displayed conservation of mass equation.

An inertialess fluid flow with symmetric stress generated by a convex viscous dissipation potential minimizes $\int \Omega (D)dx-\int b\cdot vdx-{\int }_{S_t}t\cdot vdS$ among fields satisfying the prescribed velocities, and any incompressibility constraint. The supporting-hyperplane inequality bounds the difference of two functional values below by $\int S:\nabla w-\int b\cdot w-{\int }_{S_t}t\cdot w$. Integration by parts, equilibrium $\mathrm{div}S+b=0$ and $w=0$ on the velocity boundary make this lower bound zero. A pressure reaction has zero contraction with divergence-free variations. Convexity proves global minimality without asserting uniqueness.

The Prandtl limit observes a high-Reynolds number flow within a shrinking wall-normal layer. For streamwise distance $x$ and outer speed $U$, rescale the normal distance by $\delta =\sqrt{\nu x/U}$ and the normal velocity by $U\delta /x$. Convection and normal viscous diffusion then remain comparable, while streamwise viscous diffusion is smaller. The leading normal momentum equation makes pressure independent of the rescaled normal coordinate, and the tangential equation is the Prandtl boundary-layer equation. This laminar asymptotic balance is local and need not remain valid through separation or instability.

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.