The material derivative of a polar order parameter contains rigid rotation with the local vorticity and a material-dependent alignment response to the symmetric strain-rate tensor.

The little group of a momentum $p$ is its stabilizer in the Lorentz group. Its unitary irreducible representations label the internal degrees of freedom of one-particle states on the orbit of $p$.

These stabilizer representations are used in Wigner's classification of relativistic particle states.

The Poincare algebra is generated by translations $P^{\mu }$ and Lorentz transformations $M^{\mu \nu }$. Its mixed commutator is $[M^{\mu \nu },P^{\rho }]=i({\eta }^{\nu \rho }{P}^{\mu }-{\eta }^{\mu \rho }{P}^{\nu })$.

For finite subsets $A,B,C$ of an abelian group with $A$ nonempty,An injective encoding chooses one representation of each element of $B-C$ and translates it by every element of $A$.

If a nonempty finite set $X$ minimizes $|X+B|/|X|$ among the nonempty subsets of $A$, with minimum $K^{\prime }$, thenfor every finite set $C$. Iteration is a short proof of the Plünnecke-Ruzsa inequality.

The specific impact energy is an impactor's kinetic energy divided by the target mass. For equal-density spherical bodies,

For an impactor number density per size $n(D_{\mathrm{im}})$, relative speed $v_{\mathrm{rel}}$, and collision cross-section $\sigma (D,D_{\mathrm{im}})$, the differential collision rate of one target is $n(D_{\mathrm{im}})v_{\mathrm{rel}}\sigma (D,D_{\mathrm{im}})d{D}_{\mathrm{im}}$.

The optical depth from conformal time $\eta$ to observation at ${\eta }_0$ isThe factor $e^{-\tau (\eta )}$ is the probability that a photon travels from $\eta$ to the observer without another scattering.

A connected five-point tree in phi cubed theory has three cubic vertices and two internal propagators. For labeled external legs there are fifteen diagrams: choose the leg attached to the middle vertex and partition the other four legs into two unordered pairs.

The coherent field is the ensemble mean $⟨E⟩$ of a randomly scattered wave. The zero-mean remainder $E-⟨E⟩$ is the diffuse or incoherent field.

The parabolic wave equation is a one-way, slowly varying envelope approximation to the Helmholtz equation. For carrier wavenumber $k$ and transverse coordinate $z$, a common form is $2ik{E}_x+E_{zz}=0$.

The valuation and first principal-unit digit give

If $M\times N$ is orientable, fix a point of $N$ and an ordered basis of its tangent space. Contracting a product orientation with that basis gives a smooth orientation of $M$. Interchanging the factors gives an orientation of $N$.

For a smooth $n$-manifold, the following are equivalent: an atlas with positive transition Jacobians, a smooth choice of orientation of every tangent space, and a nowhere-vanishing smooth $n$-form. Positive charts orient their coordinate frames; oriented frames select the positive local coordinate volume forms, which a partition of unity glues; and a nonzero top form declares a frame positive when its value on that frame is positive.

A function is operator concave when its negative is operator convex. Equivalently, the defining operator-convexity inequality is reversed.

If $\mathbf{P}\ne \mathbf{NP}$, then NP contains a decision problem that is neither in P nor NP-complete.

Quadratic-equation satisfiability over ${\mathbb{F}}_2$ asks whether a finite system of polynomial equations of degree at most two over the finite field ${\mathbb{F}}_2$ has a common solution.

The Boolean satisfiability problem asks whether a Boolean formula has an assignment making it true. A complete assignment is a Boolean valuation: it gives every Boolean variable a truth value, consistently at all occurrences. A satisfying assignment, also called a model of a Boolean formula, makes the entire Boolean formula true. A solver's answer SAT asserts that at least one satisfying assignment exists; UNSAT asserts that the input is an unsatisfiable Boolean formula. These are statements about existence, rather than the truth value under one particular Boolean valuation.

For conjunctive normal form, or CNF, writeEach Boolean literal $\ell$ is a Boolean variable or its logical negation. Each Boolean clause uses inclusive logical disjunction: one true Boolean literal suffices. The outer logical conjunction requires every Boolean clause to succeed under the same Boolean valuation. Choosing a different Boolean valuation for each Boolean clause would answer a different question.

Consider the original three-Boolean variable exampleAt the Boolean valuation $(x,y,z)=(0,1,0)$, the Boolean literals in $C_1$ have values $(0,1)$, those in $C_2$ have values $(1,0)$, and those in $C_3$ have values $(0,1)$. Every Boolean clause therefore has truth value one, so $F$ has truth value one: this is a satisfying assignment.

The complete truth table has $2^3=8$ Boolean valuations. Exactly two are satisfying assignments: $(0,1,0)$ and $(1,0,1)$. In particular, $(1,1,1)$ satisfies the first two Boolean clauses but falsifies the third, so satisfying most Boolean clauses does not suffice for SAT. That different objective belongs to maximum satisfiability.

A short proof of completeness avoids listing all eight rows. If $x=0$, the Boolean clause $C_1$ forces $y=1$, after which $C_3$ forces $z=0$; $C_2$ is already true. If $x=1$, $C_2$ forces $z=1$, after which $C_3$ forces $y=0$; $C_1$ is already true. These two cases exhaust the truth values of $x$ and produce precisely the two satisfying assignments above.

Verifying a satisfying assignment and finding a satisfying assignment are different tasks. Given a candidate, evaluate the Boolean formula once; finding a candidate requires choosing compatible truth values across all its Boolean clauses. The Cook-Levin theorem says that general SAT is NP-complete, even though a proposed satisfying assignment is efficiently checkable. Its proof encodes a bounded nondeterministic computation as a Boolean formula whose satisfying assignments describe accepting computations. Stephen A. Cook's 1971 paper established this connection between computational complexity theory and propositional logic.^[ref]

A concrete use is checking finite configuration rules. Let $x$ mean that an optional feature is enabled, $y$ that a fallback is selected, and $z$ that the feature's required component is installed. Require a feature or fallback, require the component whenever the feature is enabled, and forbid using the fallback with that component. Logical implication rewrites the second rule $x\to z$ as $\neg x\vee z$; De Morgan's laws rewrite the third rule $\neg (y\wedge z)$ as $\neg y\vee \neg z$. Their logical conjunction is exactly $F$, so its two satisfying assignments give the two allowed configurations. The usefulness of SAT lies in expressing local rules uniformly while demanding one globally consistent Boolean valuation. The conclusion concerns exactly these encoded rules.

Ordinary least squares minimizes the unweighted sum of squared residuals between observed and fitted responses.