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A tensor-product operator factors as $A\otimes B$ on a tensor product of two spaces. If both factors are positive semidefinite operators, their tensor-product operator is positive: a tensor-product basis of eigenvectors has eigenvalues $a_i{b}_j\ge 0$. This positivity underlies the necessary direction of the positive partial transpose criterion.

For a linear operator $H$ mapping transmitted signals to recorded signals, the positive operator $H^{\ast }H$ describes adjoint back-propagation after recording. In a reciprocal array, physical time reversal acoustics implements this operation up to a final complex conjugation and the chosen signal conventions.

In the Heisenberg picture, an observable without explicit time dependence hasHere $\hslash$ is the reduced Planck constant. An emitted photon on a transition $n\to m$ with $E_n>E_m$ has positive angular frequency $(E_n-E_m)/\hslash =|{\omega }_{mn}|$. The sign of the array's quantum phase follows its index convention; it is not a negative measured photon frequency. The frequencies involve differences of energy levels, rather than the absolute values $E_n/\hslash$. Adding a constant to $H$ leaves these frequencies unchanged.

Insert the complete orthonormal basis between two linear operators:The sum runs over intermediate states. The second identity follows by cancellation of $E_k$; it makes every term in the product acquire the same time factor $e^{i{\omega }_{mn}t}$. This is a useful reason for matrix multiplication: it composes the two indexed arrays consistently with the addition of transition frequencies. It does not multiply matching entries independently. Swapping $A$ and $B$ generally changes the intermediate-state sum and hence the result.

On all real-valued functions on $\mathbb{R}$, define $D_{r}f(x)=f(x+r)-f(x)$. The operators commute, and every real number except $-1$ is an eigenvalue. For eigenvalue $\lambda \ne -1$, values may be chosen independently on the cosets of $r\mathbb{Z}$ and extended by $f(x+r)=(1+\lambda )f(x)$, so every eigenspace is infinite-dimensional.

For a linear operator on a real vector space, the identity $T^3=T$ gives an explicit eigenspace decompositionIndeed, the linear operatorssum to $I$. Their images lie respectively in the eigenspaces for $0$, $1$, and $-1$, since $T{P}_0=0$, $T{P}_{+}=P_{+}$, and $T{P}_{-}=-P_{-}$. On each of these eigenspaces, its corresponding $P$ is the identity and the other two vanish, which proves uniqueness of the decomposition. Thus a finite-dimensional tripotent linear operator admits a diagonalization with diagonal entries in $\{0,1,-1\}$. The condition $T^2\ne T$ is equivalent to the presence of a nonzero $-1$ eigenspace, and $T^2\ne I$ is equivalent to a nonzero kernel.

A densely defined linear operator on a complex Hilbert space is self-adjoint when its adjoint operator has exactly the same domain and agrees there. Symmetry only requires $A\subseteq A^{\ast }$ and does not guarantee self-adjointness. A self-adjoint operator is a closed operator with real spectrum. Domains matter for differential operators and unbounded multiplication operators.

If a family of bounded linear operators from a Banach space to a normed space is pointwise bounded, then their operator norms are uniformly bounded.

A matrix similarity implemented by a unitary matrix. It expresses the same linear operator in two orthonormal bases, preserving eigenvalues, trace and singular values. For a Hermitian matrix, the spectral theorem gives a diagonal representative by unitary similarity.

Variational regularization balances data fidelity against a lower-semicontinuous penalty, for example by minimizing $\frac{1}{2}\Vert Au-f{\Vert }^2+\alpha J(u)$.

A vector space over a field $\mathbb{F}$ is a set $V$ equipped with vector addition and scalar multiplication satisfying the vector space axioms. Its vectors can represent directed displacements, lists of measurements, polynomials, or entire functions. The common structure is that adding two elements and multiplying an element by a scalar remain meaningful operations inside the same space.

For example, ${\mathbb{R}}^2$ uses coordinatewise addition and scaling. Withwe have $v=2b_1+b_2$. Moving twice along $b_1$ and once along $b_2$ reaches the same vector as five horizontal and four vertical steps. The two descriptions refer to different bases of the same space. A basis makes such descriptions unique; a spanning set makes them possible.

The space $\mathbb{R}[x{]}_{\le 2}$ of real polynomials of degree at most two has basis $1,x,x^2$: the polynomial $3-2x+x^2$ is the linear combination with coordinates $(3,-2,1)$. The zero vector here is the zero polynomial, and addition combines coefficients. A derivative is a linear map on this space; its matrix records how it acts on the three basis vectors. The same coordinate machinery therefore handles both geometric motion and operations on functions.

The choice of field matters. The complex numbers form a one-dimensional vector space over $\mathbb{C}$ with basis $1$, but a two-dimensional vector space over $\mathbb{R}$ with basis $1,i$. The question "how many independent directions?" has an answer only after the allowed scalars are specified. Likewise, polynomials of degree exactly two do not form a vector space: subtraction can remove the leading term, and the zero polynomial is absent.

The usefulness of the abstraction is that a linear map is determined by its values on a basis. Once those images are known, linear combinations give its action on every vector. The rank-nullity theorem relates the number of attainable outputs to the number of invisible input directions, while change of basis lets us choose coordinates suited to the problem. Eigenvectors, for example, choose directions on which repeated application of a linear operator becomes scalar multiplication.

A historical source is Hermann Grassmann's Die lineale Ausdehnungslehre (1844). Its full title advertises applications to other branches of mathematics, statics, mechanics, magnetism and crystallography; the original edition is digitized by ETH-Bibliothek. This gives an early setting for treating geometric quantities algebraically across different applications, rather than attributing the modern axiom list to that title alone.^[ref]

For a fixed homogeneous absorber in a narrow-beam geometry, define the X-ray spectral attenuation operator on L1 space over a bounded positive energy interval. A physical input $f(E)\ge 0$ is an integrable function giving incident photon rate density. Let its linear attenuation coefficient $\mu (E)\ge 0$ be a measurable function, with $x\ge 0$. The transmitted uncollided density isHere $w(E)\ge 0$ is a fixed bounded detector response described by a measurable function; background has been subtracted. This is a positive linear operator on L1 space. Since $0\le e^{-\mu (E)x}\le 1$, it cannot increase the integrated absolute incident rate. Multiplication of exponentials proves $A_{x+y}=A_x{A}_y$ for successive layers of the same material. Ignore secondary radiation entering the detector, saturation and changes to the source. Discrete spectral lines use the finite-sum version below instead of an ordinary rate density.

For a chosen filter, stipulate ${\mu }_{\alpha }=0.20\mathrm{m}{\mathrm{m}}^{-1}$, ${\mu }_{\beta }=0.80\mathrm{m}{\mathrm{m}}^{-1}$ and input line rates $(N_{\alpha },N_{\beta })=(4,1)$ in a common arbitrary rate unit. Assume equal calibrated detector weight for both lines. At $x=2.0\mathrm{mm}$,The selected $8.5\mathrm{keV}$ line's fraction rises from $4/5=0.800$ to $0.929974$, but its absolute rate falls by about $33.0\%$. Filtering improves purity at the cost of signal. It multiplies existing components and cannot create a new emitted spectral line.

With just one integrated reading, $S=N_{\alpha }+N_{\beta }$, the two unknown line rates are underdetermined. If the source is unchanged between an empty-path reading and a filtered reading, the ideal inverse problem becomesThe determinant $e^{-1.6}-e^{-0.4}\simeq -0.468424$ is nonzero, so these two ideal readings determine the two stipulated line rates uniquely. Identical coefficients would give zero determinant and destroy that separation. This small linear algebra example explains why absorber contrast supplies information, while also showing its limit: unknown scattered components or unknown detector weights add unknowns that two readings cannot determine.

Zero is an eigenvalue of a linear operator exactly when the operator has a nontrivial kernel. Its eigenvectors are precisely the nonzero vectors in that kernel.

For $H=\alpha a^{†}a+\Delta (a^2+a^{†2})/2$, a finite real squeeze requires $\alpha >|\Delta |$. At $\alpha =\Delta >0$, the Hamiltonian instead equals $\alpha Q^2-\alpha /2$, with $Q=(a+a^{†})/\sqrt{2}$. The formal oscillator frequency is zero, but this operator is not constant: $⟨2|H|0⟩=\alpha /\sqrt{2}$. Thus a normalizable oscillator ground state and finite canonical squeeze cannot be obtained by simply substituting zero frequency into the positive-frequency formula.