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Using the estimator from part (b) and the spectral decomposition of $X^{T}X$,ThusUnder the stated separation of scales, the shrinkage factor is approximately one for $i\le q$ and approximately zero for $i>q$. The principal-component shrinkage by ridge regression formula therefore reduces approximately tothe orthogonal projection of $Y$ onto the span of the $q$ normalized sample principal components with greatest variance.

Compactness gives a stronger statement than the one requested. Since $T(B_H)$ has compact closure, finitely many open balls of radius $\delta$ cover it. Choose their centres from $T(B_H)$ and let $E$ be their finite-dimensional linear span. ThenFor the orthogonal projection $P_E$, best approximation in a Hilbert space givesIn particular this holds for every vector $e_n$ in the given orthonormal basis.

The operator $P_{E}T$ has image in $E$ and hence has finite rank, whileThus every compact operator is an operator norm limit of finite-rank operators. This is the finite-rank approximation theorem for compact operators on a Hilbert space.

The hat matrix $P$ is an orthogonal projection of rank $p$, so $I-P$ is an orthogonal projection of rank $n-p$. Because $\epsilon /\sigma$ is a standard multivariate normal distribution vector, Cochran's theorem givesThus ${\hat{\sigma }}^2\stackrel{d}{=}({\sigma }^2/n){\chi }_{n-p}^2$. Substituting this into the preceding formula gives the Distribution of the Akaike information criterion in a normal linear model:

For fixed $A$, differentiating under the constraint ${\sum }_i{z}_i=0$ givesBecause the columns $u_j$ are orthogonal,The fitted value is therefore $\overline{X}+{\Pi }_A(X_i-\overline{X})$, where ${\Pi }_A$ is the orthogonal projection onto the column space of $A$.

The residual sum of squares is minimized by choosing this column space to be the span of the $d$ leading eigenvectors ofThus $\hat{A}$ may be taken to have those orthonormal eigenvectors as columns. Their nonzero scales are immaterial because inverse scaling of the scores leaves $A{z}_i$ unchanged.

True. The matrix $A^{†}A$ is the orthogonal projection onto the row space $\mathcal{R}(A^H)$, so its eigenvalues are zero and one. Consequently

Let ${\Pi }_G$ be the orthogonal projection onto $G$. The two reflection operators areThe first fixes the hyperplane $|\psi {⟩}^{\perp }$ and changes the sign of $|\psi ⟩$; the second fixes $G^{\perp }$ and changes the sign of $G$.

Write the normalized projections of $|\psi ⟩$ asThe amplitude amplification theorem states that forone has, up to the irrelevant global sign $(-1{)}^k$,Thus every iteration increases the angle toward the good axis by $2\theta$ until the first overshoot.

For the proof, the plane $\mathrm{span}\{|g⟩,|b⟩\}$ is invariant. In its ordered basis,Their product is a planar rotation through $2\theta$ together with an overall sign. Applying that matrix $k$ times proves the formula, while components orthogonal to this plane never enter the initial state.

The defining relation for $P_{\mathcal{U}}$ givesPart (a) therefore shows that $P_{\mathcal{U}}f$ is the best approximation. Moreover, $P_{\mathcal{U}}f$ and $f-P_{\mathcal{U}}f$ are orthogonal, soThus orthogonal projection is a contraction:

Let ${\Pi }_{\mathcal{G}}$ be the orthogonal projection onto the good linear subspace and writewhere the two displayed states are normalized projections into $\mathcal{G}$ and ${\mathcal{G}}^{\perp }$. Define the reflectionsThe amplitude amplification iterate $Q=R_{\psi }{R}_G$ preserves the good-bad plane, and its $k$th iterate satisfiesThus repeated reflections rotate amplitude toward the good subspace, reaching constant success probability after $O(1/\sin \theta )$ iterations.

Let a projective measurement have mutually orthogonal projections $P_i$ satisfying ${\sum }_i{P}_i=I$. On a density operator $\rho$, the Born rule and the Lüders rule giveconditional on outcome $i$. On the first part of a bipartite quantum system, replace $P_i$ by $P_i\otimes I_2$.

The subsystems are isolated when there is no interaction term coupling them. Their Hamiltonian operator has the formso their subsequent unitary time evolution factorizes as $U_1\otimes U_2$.

The reduced density matrix of subsystem 1 iswhere the partial trace is characterized byfor every local observable $M_1$. Thus ${\rho }_1$ contains exactly the statistics accessible by measurements on subsystem 1.

Suppose a projective measurement $\{Q_j\}$ is performed on subsystem 2 and its outcome is not communicated. The resulting nonselective state isFor every $M_1$, the cyclic property of the trace and ${\sum }_j{Q}_j^2=I_2$ giveHence ${\rho }_1^{\prime }={\rho }_1$. No local projective measurement on subsystem 1 can reveal whether the remote unreported measurement occurred. This is quantum no-signalling, which prevents a choice made at a spacelike separation from transmitting information faster than light and makes the measurement formalism compatible with relativistic causality.

The proposed nondisturbing device would violate no information without disturbance. In an ordinary quantum instrument, let $M_{i\alpha }$ be the Kraus operators associated with classical output $i$. If every pure state $|\psi ⟩$ remains unchanged even conditional on the displayed outcome, every nonzero $M_{i\alpha }|\psi ⟩$ must be parallel to $|\psi ⟩$. A linear operator for which every vector is an eigenvector is a scalar multiple of the identity, so $M_{i\alpha }=c_{i\alpha }I$. Its output probabilityis independent of the state. It cannot equal $⟨\psi |P_i|\psi ⟩$ for arbitrary projectors. Equivalently, repeated nondisturbing samples would permit quantum state tomography of one specimen and then quantum cloning, contradicting ordinary quantum theory.

Such devices would not necessarily enable superluminal signalling. One consistent operational extension could make every sequence of outputs depend only on the local reduced density matrix and local settings. Since an unreported remote measurement leaves that matrix unchanged, all local device statistics would remain unchanged too. Other extensions could add nonlocal outcome-dependent rules and permit signalling, but that behavior is additional to the device specification rather than forced by it.

Normalize the stated Pauli matrices asThe factor $3^{-1/2}$ changes only the overall normalization at fixed $N$. In the spin-one Cartesian basis, the associated periodic uniform matrix product state isThis is the Pauli-matrix representation of the Affleck--Kennedy--Lieb--Tasaki state.

The Pauli matrix multiplication lawshows that products on two neighboring sites span all of $M_2(\mathbb{C})$, so the tensor is an injective matrix product state after blocking two sites. More geometrically, its two-site image is the scalar plus antisymmetric subspace of $3\otimes 3$, namely the total-spin $J=0$ and $J=1$ sectors. By the Two-site support of the Pauli-matrix Affleck--Kennedy--Lieb--Tasaki tensor, the missing subspace is the five-dimensional symmetric traceless $J=2$ sector.

Let $P_{n,n+1}^{(2)}$ be the orthogonal projection onto that $J=2$ sector. The parent Hamiltonian of a matrix product state isEach term annihilates $|{\Psi }_N⟩$, so this is a frustration-free quantum Hamiltonian and the MPS is a ground state. Writing $x={\mathbf{S}}_n\cdot {\mathbf{S}}_{n+1}$ and using the eigenvalues $-2,-1,1$ in the three total-spin sectors gives the explicit projectorThis is the Affleck--Kennedy--Lieb--Tasaki parent Hamiltonian. Injectivity implies that its periodic ground state is unique for every sufficiently long chain.

Let $L_n=\mathrm{span}\{e_1,\dots ,e_n\}$ and let $P_n$ be its orthogonal projection. For $z\in \mathbb{C}$, defineBecause $A$ is unitary,The displayed finite Hermitian matrix uses only finitely many matrix entries of $A$, and its least eigenvalue can be approximated by an arithmetic algorithm in the SCI hierarchy.

As $L_n$ increases densely,A unitary operator is normal, so the spectral theorem for normal operators on a separable Hilbert space identifies the limit asThe functions ${\gamma }_n$ are continuous and decrease to a continuous function on the compact unit circle. The Dini theorem therefore gives uniform convergence there.

Take successively finer rational meshes $G_n$ around $\mathbb{T}$. From the finitely computed values of ${\gamma }_n$, retain the mesh minima in the comparison neighborhoods whose radii are ${\gamma }_n(z)$; equivalently, use the standard local-minimum construction for a decreasing approximation to a distance function. Call the resulting finite set ${\Gamma }_n(A)$. Uniform convergence and the shrinking mesh implyin Hausdorff distance. Every operation at stage $n$ is finite and arithmetic, so $({\Gamma }_n)$ is the required one-limit sequence.

There is no finite-stage certificate that the whole output has the correct Hausdorff error: the convergence of ${\gamma }_n$ has no uniform computable rate over all unitary operators, and unseen matrix entries can still reveal a missing spectral component. A small computed residual can certify that an individual output point lies near the spectrum, but it cannot verify that ${\Gamma }_n(A)$ covers all of $\mathrm{Sp}(A)$. Thus the full finite-stage output is not verifiable without additional information.

A projection-valued measure on the Borel sets of $\mathbb{C}$ is a map $E$ into the orthogonal projections on a separable Hilbert space $\mathcal{H}$ such thatand for pairwise disjoint $B_j$,for every $v\in \mathcal{H}$, with convergence in norm.

The spectral theorem for normal operators on a separable Hilbert space states that a bounded normal operator $A$ has a unique projection-valued measure supported on $\mathrm{Sp}(A)$ for whichMore generally, the Borel functional calculus for a normal operator is

For $v,w\in \mathcal{H}$, the scalar spectral measures areIf $A$ is self-adjoint, its spectrum and hence the support of $E$ lie in $\mathbb{R}$. Moreover,so ${\mu }_v$ is a positive measure, andThe paper prints total mass $\Vert v\Vert$; with the standard definition it is $\Vert v{\Vert }^2$, so the unsquared norm is a typographical error.

An element $x$ of a unital C-star algebra is positive when $x=y^{\ast }y$ for some $y$, equivalently when $x=x^{\ast }$ and $\sigma (x)\subseteq [0,\infty )$. The continuous functional calculus for the nonnegative function $t\mapsto \sqrt{t}$ defines a positive element $x^{1/2}$ satisfying $(x^{1/2}{)}^2=x$. If a positive $z$ also satisfies $z^2=x$, functional calculus for $z$ gives $z=\sqrt{z^2}=\sqrt{x}$, proving uniqueness. For a positive operator $T=S^{\ast }S$ on ${\ell }^2$,

For arbitrary $T$, put $|T|=(T^{\ast }T{)}^{1/2}$. Thenso $\ker |T|=\ker T$. Defineon $\mathrm{im}|T|$. The kernel identity makes this well-defined, and the norm identity makes it an isometry. Extend it continuously toand set it equal to zero on $\ker T$. The resulting $U$ is a partial isometry, has $\ker U=\ker T$, and satisfies the polar decomposition of a bounded operator $T=U|T|$.

Finally $U^{\ast }U$ is the orthogonal projection onto $(\ker T{)}^{\perp }$, which contains the range of $|T|$. Therefore

Conditional expectation is the orthogonal projection from $L^2(\mathcal{F})$ onto $L^2(\mathcal{G})$. The difference $X-\mathbb{E}[X\mid \mathcal{G}]$ is orthogonal to every $\mathcal{G}$-measurable square-integrable variable, including $\mathbb{E}[X\mid \mathcal{G}]-\mathbb{E}[X\mid \mathcal{H}]$. The Pythagorean theorem in an inner-product space applied togives the identity.

Because the two flags $E_C$ and $F_C$ are orthogonal projections, the flagged state is block diagonal. If $h(\lambda )$ denotes the binary entropy, thenandIts unflagged marginals are ${\rho }_{AB}=\lambda {\rho }_{AB}^{\prime }+(1-\lambda ){\rho }_{AB}^{\prime \prime }$ and ${\rho }_B=\lambda {\rho }_B^{\prime }+(1-\lambda ){\rho }_B^{\prime \prime }$. Substitution in Strong subadditivity of Von Neumann entropy cancels the two binary-entropy terms and givesThis is exactly the concavity of quantum conditional entropy.

Both $P_m$ and the Leray-Helmholtz projection are orthogonal projections, hence contractions in $L^2$. Taking the $H$ norm of the first Galerkin equation givesTherefore

Take the $L^2$ inner product of the first Galerkin equation with ${\omega }_m$. The orthogonal projection may be removed against ${\omega }_m\in {\tilde{H}}_m$, and skew-symmetry of incompressible transport cancels the nonlinear term. HenceThe Cauchy-Schwarz inequality and Young inequality giveThus both quantities requested in the first estimate are bounded by, for example,

Next take the inner product with $-\Delta {\omega }_m$. Periodicity and incompressibility giveThe given curl identity and the two-dimensional Gagliardo-Nirenberg inequality implyApplying Young's inequality to this term and to $(g,-\Delta {\omega }_m)$ yieldsThe first estimate bounds the right-hand side independently of $m$. Therefore one may choose a constant $R_2=R_2(\Vert g{\Vert }_2,\gamma ,\nu ,{\mu }_1)$ such that

The Petrov-Galerkin method chooses an approximate solution $u_h$ in a finite-dimensional trial vector space $V_h$ and tests a weak formulation against a possibly different test space $W_h$. For a bilinear form $a$ and a linear functional $\ell$, it imposes $a(u_h,v_h)=\ell (v_h)$ for every $v_h\in W_h$. Taking $W_h=V_h$ recovers the usual Galerkin method. Different test functions can alter stability and accuracy; the Galerkin orthogonality is then against the test space and need not be an orthogonal projection in an energy norm.

For the overlapping monochromatic wave components, let $d$ be the thickness and ${\lambda }_0$ the vacuum wavelength. Define $\Delta n=n_s-n_f>0$ using the slow and fast refractive indices. With complex amplitude convention $e^{i(kz-\omega t)}$, the accumulated slow-minus-fast phase difference isThis is the optical path difference $\Delta nd$ expressed as a wave phase. It is a signed accumulation convention; with the opposite time convention the sign reverses. The outgoing polarization of an electromagnetic wave depends on $\delta$ modulo $2\pi$. Here the positive slow-minus-fast $\Delta n$ differs from the signed principal-index $n_e-n_o$ used in the parent birefringence entry when $n_e<n_o$.

For an original worked example, stipulate $\Delta n=0.0100$ at ${\lambda }_0=600\mathrm{nm}$ and a uniform thickness $d=15.0\mu \mathrm{m}$. These are design assumptions, not measured material data. The optical path difference is $150\mathrm{nm}$, soIt is a quarter-wave plate. At the same wavelength and refractive indices, doubling $d$ to $30.0\mu \mathrm{m}$ gives $\delta =\pi$, a half-wave plate. A $1.0\mu \mathrm{m}$ thickness increase changes $\delta$ by $\pi /30$, or $6^{\circ }$; retaining the same birefringence at $500\mathrm{nm}$ would instead give $\delta =3\pi /5$ for the $15.0\mu \mathrm{m}$ plate. Real refractive indices also vary with wavelength, so this last calculation explicitly neglects optical dispersion.

For these equal transmitted wave amplitudes, orthogonal projection onto a polarizer crossed with the initial $45^{\circ }$ linear polarization gives a simple independent readout. After removing a common wave phase, the component complex amplitudes are proportional to $(1,e^{i\delta })/\sqrt{2}$; orthogonal projection onto $(1,-1)/\sqrt{2}$ gives $(1-e^{i\delta })/2$. Squaring its modulus yields $I_{\perp }/I_{\mathrm{in}}={\sin }^2(\delta /2)$: zero at $d=0$, one half at $15\mu \mathrm{m}$, and one at $30\mu \mathrm{m}$. This assumes ideal polarizers, negligible specular reflection and wave absorption, overlapping components and stable relative wave phase. It measures a periodic function of phase retardation, so one brightness measurement cannot determine the thickness uniquely.