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Since every eigenvalue has modulus less than one, $1$ is not an eigenvalue and $I-H$ is invertible. Hence the iteration has a unique fixed point $\hat{x}=(I-H{)}^{-1}v$. The stated consistency implication gives $A\hat{x}=b$, and nonsingularity of $A$ identifies $\hat{x}=x^{\ast }$. This justifies using the fixed-point relation even though consistency was stated in only one direction.

Expand the initial error in the eigenvector basis, $x^0-x^{\ast }={\sum }_i{c}_i{w}_i$. Subtract the fixed-point equation from the stationary iterative method for a linear system and iterate:With $q={\max }_i|{\lambda }_i|<1$, any norm obeys $\Vert x^k-x^{\ast }\Vert \le q^k{\sum }_i|c_i|\Vert w_i\Vert \to 0$. ThusThe argument uses the given diagonalizable eigenbasis; the more general convergence criterion is spectral radius less than one.

The Time-dependent Schrödinger equation is $i\hslash {\partial }_t\Psi =H\Psi$. The normalized eigenstates of the Hermitian Hamiltonian $H_3$ with distinct eigenvalues are orthogonal, so they form an orthonormal basis of this two-state system. Substitute the expansion of $\Psi$ and compare the two coefficients:Hence $a_0(t)=a_0(0)e^{it/\hslash }$ and $a_1(t)=a_1(0)e^{-it/\hslash }$. For the normalized test state $\varphi =\alpha {\chi }_0+\beta {\chi }_1$, the Born rule givesThe complex conjugates are essential; relative phases can affect this probability even though the energy-state populations remain constant under $H_3$.

In the ordered eigenbasis, $H_1=\left(\begin{array}{cc}0& 1\\ 1& 0\end{array}\right)$, a Pauli matrix. The coefficient equations are $i\hslash {\dot{a}}_0=a_1$ and $i\hslash {\dot{a}}_1=a_0$, with $a_0(0)=1$, $a_1(0)=0$. Differentiating the first gives ${\ddot{a}}_0=-a_0/{\hslash }^2$, and its initial derivative vanishes. ThusThe population of ${\chi }_1$ is ${\sin }^2(t/\hslash )$. To be an $H_3$ eigenstate of eigenvalue $+1$, the coefficient of ${\chi }_0$ must vanish. Its first positive zero isThe global phase does not change its eigenstate or measurement probabilities.

Let $G$ be a finite undirected $d$-regular graph on $N$ vertices, with adjacency matrix $A$ and normalized random walk matrix $P=A/d$. Define edge expansion and normalized conductance byAn expander family has orders tending to infinity, uniformly bounded maximum degree, and a uniform positive lower bound on $h$. For a regular graph the constant vector is an eigenvector of $P$ with eigenvalue one. Write its eigenvalues as $1={\mu }_1\ge {\mu }_2\ge \cdots \ge {\mu }_N\ge -1$, and let $\delta =1-{\mu }_2$. This spectral gap measures the energetic cost of departing from a constant function.

For every mean-zero vector $f$, the spectral theorem and the graph's energy identity giveApply this to $f=1_S-|S|1/N$. Its norm squared is $|S|(1-|S|/N)$ and its energy is $e(S,S^c)/d$. ThereforeThus a uniform spectral gap gives uniform edge expansion when $d$ is fixed. This also bounds exterior vertex expansion: each exterior vertex accounts for at most $d$ boundary edges, so $|N(S)\setminus S|\ge e(S,S^c)/d$.

For the converse, take a mean-zero eigenvector $f$ for ${\mu }_2$ and choose its sign so that its positive support has at most $N/2$ vertices. Put $g=f^{+}$, which is nonzero. Truncation decreases energy in the following precise sense: for any two real numbers $a,b$,Summing over edges yields $⟨g,(I-P)g⟩\le ⟨g,(I-P)f⟩=\delta \Vert g{\Vert }_2^2$. For the threshold sets $S_t=\{v:g(v{)}^2>t\}$, every nonempty set lies in that small positive support. Integrating its boundary count gives the layer-cake identityThe Cauchy-Schwarz inequality bounds the left side bybecause the first sum is at most $d\delta \Vert g{\Vert }_2^2$ and the second at most $2d\Vert g{\Vert }_2^2$. Cancelling proves the Cheeger inequality in both directions:Hence bounded-degree regular families have uniform edge expansion exactly when their second-largest normalized eigenvalue stays uniformly below one.

Absolute eigenvalues give additional information. Put $\rho ={\max }_{i\ge 2}|{\mu }_i|$. Centering the indicators of $S,T$ and applying the operator-norm bound on the mean-zero subspace proves the expander mixing lemma:Here $e(S,T)=1_S^{T}A{1}_T$, so internal edges are counted as oriented incidences. A small $\rho$ makes edge counts resemble uniformly distributed edges. It also gives rapid random walk mixing: starting at a vertex, $P^t$ contracts the mean-zero component of its point mass by at most ${\rho }^t$ in Euclidean norm; Cauchy-Schwarz inequality then gives total variation distance at most $\frac{1}{2}\sqrt{N}{\rho }^t$ from the uniform distribution. A bipartite graph has eigenvalue $-1$ and its non-lazy walk alternates sides, even if it has good edge expansion. The lazy walk $(I+P)/2$ has nonconstant eigenvalues between zero and $1-\delta /2$, resolving this periodicity. It is important to distinguish the gap $1-{\mu }_2$ controlling cuts from the absolute bound controlling non-lazy mixing.

Here is a fully specified deterministic construction of an infinite family. Its degree is large but fixed. SetChoose the lexicographically first ordered list $a_1,\dots ,a_d\in W$ satisfyingThis finite deterministic search terminates. To prove existence, choose the list uniformly and independently. For each nonzero $u$, its signs are independent unbiased signs; the exponential-moment estimate $\cosh t\le e^{t^2/2}$ and Markov inequality give an exceptional probability at most $2e^{-d/200}$. A union bound gives total failure probability at mostThus at least one qualifying list exists. Fix it once and for all.

Let $H$ be the Cayley graph on $W$ whose $j$th neighbor of $x$ is $x+a_j$. It is an undirected $d$-regular port-labeled graph, allowing repeated edges and loops in this intermediate construction. Its characters $x\mapsto (-1{)}^{u\cdot x}$ form an orthogonal eigenbasis, with the displayed averages as normalized eigenvalues. Therefore $\rho (H)\le 1/10$. Each loop counts as one transition port; degrees here mean row sums of the adjacency matrix.

We next define the zig-zag product and prove the bound used for iteration. A port labeling of a $D_0$-regular undirected graph $F$ gives an involution ${\mathrm{Rot}}_F(v,a)=(w,b)$, recording the endpoint and reverse port. If a $d$-regular graph $H_0$ has vertex set $[D_0]$, the product has vertices $(v,a)\in V(F)\times [D_0]$. For each ordered pair of small-graph ports, move from $a$ to $a^{\prime }$ in $H_0$, follow the big edge ${\mathrm{Rot}}_F(v,a^{\prime })=(w,b^{\prime })$, and move from $b^{\prime }$ to $b$ in $H_0$. The endpoint is $(w,b)$; reversing the steps reverses the two small-graph port labels. The product is undirected, has $|V(F)|D_0$ vertices, and has degree $d^2$.

Write $B=I\otimes P_{H_0}$ and let $R$ be the permutation matrix of ${\mathrm{Rot}}_F$. The normalized product operator is $BRB$. For a mean-zero unit vector, decompose $f=u+v$ into its cloud-constant part and its cloud-orthogonal part. Then $Bu=u$, $\Vert Bv\Vert \le \rho (H_0)\Vert v\Vert$, and $\Vert R\Vert =1$. The compression of $R$ to cloud-constant vectors is exactly $P_F$, so $|⟨u,Ru⟩|\le \rho (F)\Vert u{\Vert }^2$. Expanding the quadratic form and applying Cauchy-Schwarz inequality givesThe operator is symmetric, so its mean-zero operator norm is bounded by this same quantity. No unproved product spectral estimate is needed.

Start with the loop-inclusive complete graph $G_0$ on $D$ vertices, whose normalized matrix is $J/D$ and whose nonconstant eigenvalues are all zero. Given a $D$-regular $G_t$, its two-step graph $G_t^2$ has degree $D^2$ and normalized operator $P_{G_t}^2$. Label a two-step edge by its ordered pair of ports; reversal reverses the path and its reverse ports. These $D^2$ labels are the vertex set of the fixed graph $H$. DefineIt has degree $d^2=D$, and its number of vertices is $N_{t+1}=N_t{D}^2$, hence $N_t=D^{2t+1}$. By the proved bound,Starting from zero, induction gives $\rho (G_t)\le 1/2$, since $(1/2{)}^2+21/100=46/100<1/2$. The cut-energy argument therefore gives multiplicity-counted edge expansion at least $D/4$ for every $G_t$.

To obtain ordinary simple graphs, delete loops and merge parallel edges. The maximum degree is at most $D$, and any one unordered vertex pair had multiplicity at most $D$. Thus every cut retains at least $1/D$ of its multiplicity-counted boundary, givingThese simple graphs form an expander family. Every choice is deterministic: the finite base search is fixed independently of $t$, and the recursive edge rules determine all subsequent adjacency lists. Once that base is fixed, adjacency lists can be generated successively using the previous lists, with a constant amount of work per output edge. This is squaring and zig-zag iteration yields bounded-degree expanders. The spectral argument supplies the uniform expansion guarantee and explains why squaring improves the eigenvalues while the product restores a constant degree.

Write $\Gamma =S{L}_2(\mathbb{Z})$ and use the determinant-normalized slash operatorThe automorphy factor identity proves $(f{|}_k\alpha ){|}_k\beta =f{|}_k(\alpha \beta )$, and positive scalar matrices act trivially. Let ${\mathcal{M}}_n$ be all integral matrices of determinant $n>0$. It is stable under left and right multiplication by $\Gamma$. Its left orbits have unique representativesIndeed Bézout's identity reduces the first column to $(a,0)$, the determinant fixes $d=n/a$, and a row shear reduces $b$ modulo $d$. These are the determinant-n matrix representatives for Hecke operators. For composite $n$, ${\mathcal{M}}_n$ can contain more than one double coset; keeping every integral matrix, including nonprimitive ones, is essential.

Define the Hecke operator byChanging a representative on the left leaves its summand unchanged. Right multiplication by $\Gamma$ permutes left orbits of ${\mathcal{M}}_n$, so the result satisfies the weight-$k$ transformation law. Each summand is holomorphic. Its expansion at infinity is bounded, and vanishes there when $f$ is a cusp form; the group has just one cusp class. Therefore $T_n$ preserves both $M_k$ and $S_k$, and $T_1$ is the identity.

For a Fourier expansion $f={\sum }_{r\ge 0}{a}_r{q}^r$, the sum over $b$ vanishes unless $d\mid r$. Substitute $r=dt$ and then collect the coefficient of $q^m$ to obtain the Fourier coefficients of a composite-index Hecke operator:Here $\gcd (0,n)=n$, so the constant coefficient is ${\sigma }_{k-1}(n)a_0$; at $m=1$ it is $a_n$. This proves preservation of integral coefficients. At a prime,with $a_{m/p}=0$ if $p\nmid m$.

We can prove the entire Hecke multiplication relations directly, not just quote them. On formal power series put $U_p(\sum a_m{q}^m)=\sum a_{pm}{q}^m$ and $V_{p}f(q)=f(q^p)$. Although these individual operators need not preserve level one, $U_p{V}_p=1$ and the coefficient formula givesMultiply on the left by $T_p=U_p+p^{k-1}{V}_p$. In the first part the $j=0$ term is $U_p^{r+1}$; every $j\ge 1$ reduces by $U_p{V}_p=1$ to $p^{j(k-1)}{V}_p^{j-1}{U}_p^{r-j}$. The second part, together with the $j=0$ term, is $T_{p^{r+1}}$. The remaining sum is $p^{k-1}{T}_{p^{r-1}}$. ThusFor coprime indices, the coefficient divisors split independently in the displayed formula, proving $T_m{T}_n=T_{mn}$ when $(m,n)=1$. Induction with the prime-power recurrence gives $T_{p^r}{T}_{p^t}={\sum }_{j=0}^{\min (r,t)}{p}^{j(k-1)}{T}_{p^{r+t-2j}}$. Combining the primes provesIn particular the modular Hecke algebra is commutative and is generated by the prime operators.

For cusp forms the Petersson inner product isCusp decay makes it finite and positive definite. The Hecke operators are self-adjoint for the Petersson inner product. To see the change-of-variables mechanism, fix a double coset $\Gamma \alpha \Gamma \subset {\mathcal{M}}_n$ and put $H=\Gamma \cap {\alpha }^{-1}\Gamma \alpha$. Representatives $\gamma$ of $H\backslash \Gamma$ give left representatives $\alpha \gamma$. Unfolding the sum in $⟨{\sum }_{\gamma }f{|}_k\alpha \gamma ,g⟩$ changes the integral over $\Gamma \backslash \mathbb{H}$ to the integral over $H\backslash \mathbb{H}$ of $(f{|}_k\alpha )\overline{g}$. The substitution $w=\alpha \tau$ preserves $dxdy/y^2$ and has $\Im w=(\det \alpha )y/|c\tau +d{|}^2$; these factors move the slash from $f$ to $g{|}_k{\alpha }^{-1}$. Refold over $\alpha H{\alpha }^{-1}=\Gamma \cap \alpha \Gamma {\alpha }^{-1}$ to get the reverse double coset. Since scalar matrices act trivially, ${\alpha }^{-1}$ has the same slash action as its adjugate ${\alpha }^{\ast }=n{\alpha }^{-1}$, an integral matrix of determinant $n$. The adjugate involution permutes all the double cosets in ${\mathcal{M}}_n$. The common real factor $n^{k/2-1}$ is unchanged, giving $⟨T_{n}f,g⟩=⟨f,T_{n}g⟩$.

Since $S_k$ is finite dimensional, commuting self-adjoint linear operators have a common orthonormal basis of Hecke eigenforms: diagonalize one operator, restrict the others to its eigenspaces, and repeat until these spaces cannot split further. If $f$ is a nonzero common Hecke eigenform with $T_{n}f={\lambda }_{n}f$, the coefficient at $q$ gives $a_n={\lambda }_n{a}_1$. Were $a_1=0$, every positive coefficient would vanish, contrary to $f\ne 0$ being cuspidal. Normalize $a_1=1$; then ${\lambda }_n=a_n$, and the Hecke multiplication relations yieldThe eigenvalues are real by self-adjointness. Their multiplicativity and prime-power recurrence give the Euler product of a Hecke eigenform, initially in an absolutely convergent half-plane:The Mellin transform in Question 5 supplies its analytic continuation and functional equation.

Finally, for positive even $k\ge 4$, $M_k=\mathbb{C}{E}_k\oplus S_k$, by subtracting the constant coefficient times the normalized Eisenstein series. The divisor identityfollows by multiplying the geometric sums at each prime; it and the constant-coefficient formula prove $T_n{E}_k={\sigma }_{k-1}(n)E_k$. Thus $M_k$ has an Eisenstein series eigenline and a basis of cuspidal Hecke eigenforms. Odd and negative weights have no forms, $M_2=0$, and on $M_0=\mathbb{C}$ the operator is the scalar ${\sigma }_{-1}(n)$. These statements fix the normalization and describe the exceptional small weights as well.

Use logarithms to base two, so all entropies are in bits. For a discrete probability distribution $p$ and a density operator $\rho$ with eigenvalues ${\lambda }_j$, the Shannon entropy and Von Neumann entropy are respectivelyUse $0\log 0=0$. The second definition is basis-independent by the spectral theorem; for a density operator diagonal with probabilities $p_x$, it agrees with the first.

The classical relative entropy, or Kullback-Leibler divergence, of $p$ from $q$ isIt is $+\infty$ if some $p_x>0$ has $q_x=0$. The quantum relative entropy iswhen the support of $\rho$ is contained in that of $\sigma$, and is $+\infty$ otherwise. Matrix logarithms are taken on the corresponding positive supports. In a common eigenbasis this reduces to classical relative entropy; both divergences are nonnegative and are generally asymmetric.

To prove Fano's inequality, let the finite alphabet size be $m\ge 2$ and let $E$ indicate an incorrect guess. Since $E$ is determined by $(X,Y)$, the chain rule for conditional entropy first givesExpanding in the opposite order givesConditioning reduces entropy, so $H(E\mid Y)\le H(E)=h_2(p_e)$, the binary entropy function. When $E=0$, knowledge of $Y$ determines $X=f(Y)$ and the conditional entropy is zero. When $E=1$, $X$ can lie in at most the $m-1$ alphabet values other than $f(Y)$. Entropy on $m-1$ values is at most ${\log }_2(m-1)$: its deficit from that number is the nonnegative relative entropy from the uniform distribution. Averaging the two cases therefore givesCombining the two expansions provesThis proof of Fano's inequality via an error indicator works for any alphabet-valued guessing rule, not just an optimal one. If $m=1$, there is no guessing uncertainty and the claim is simply $H(X\mid Y)=0$; that case is treated separately rather than writing $\log 0$.

Work with a nonsingular multivariate normal distribution, so $V$ is positive-definite. The claimed finite maximum-likelihood estimates also require the centered sample covariance matrix to be nonsingular; the exceptional case is discussed below.

Independence gives, up to a constant, the log-likelihoodLet $\bar{Y}=n^{-1}{\sum }_i{Y}_i$ and $S=n^{-1}{\sum }_i(Y_i-\bar{Y})(Y_i-\bar{Y}{)}^T$. The centered sum is zero, and expansion therefore givesFor every fixed positive-definite matrix $V$, the extra quadratic term in the log-likelihood is $-n(\bar{Y}-\mu {)}^T{V}^{-1}(\bar{Y}-\mu )/2$, with unique maximum at $\mu =\bar{Y}$.

Assume $S$ is positive-definite. Maximizing over $V$ now means minimizing $\log \det V+\mathrm{tr}(V^{-1}S)$. Put $A=S^{1/2}{V}^{-1}{S}^{1/2}$, another positive-definite matrix. ThenIf $a_1,\dots ,a_p>0$ are its eigenvalues, the variable part is ${\sum }_j(a_j-\log a_j)$. The scalar inequality $a-\log a\ge 1$, with equality only at $a=1$, shows that this is minimized uniquely at $A=I$, or $V=S$. ThusThe divisor is $n$ because this is maximum likelihood estimation, not the unbiased covariance estimate with divisor $n-1$.

The nonsingularity condition matters. The centered scatter has rank at most $n-1$. If $S$ is singular, choose $\mu =\bar{Y}$, retain positive covariance eigenvalues on its range and let a covariance eigenvalue on its null space tend to zero. The quadratic likelihood term remains finite, while $-\log \det V\to +\infty$. This is singular covariance and nonexistence of a Gaussian maximum likelihood estimate: there is no finite maximizer over nonsingular $V$. Under a nonsingular Gaussian population, $S$ is positive definite almost surely when $n>p$; for $n\le p$ it is necessarily singular. The asserted estimate and the inverse in the next part have their usual meaning under $n>p$.

Let $g>0$ be the magnitude of the downward gravity and write ${\alpha }_T$ for the thermal expansion coefficient, to distinguish it from the dynamo coefficient of the previous question. The conductive state has $\mathbf{u}=0$, $\mathbf{B}=B_0\hat{\mathbf{z}}$ and $T_0(z)=T_r-\Delta Tz/d$. Its gravitational term is absorbed in a background hydrostatic pressure.

For the dimensional two-dimensional fields, choose the stream function and Cartesian magnetic flux function conventionsThese automatically give zero divergence. Define the Jacobian determinant bracket $J(f,h)=f_x{h}_z-f_z{h}_x$, so $\mathbf{u}\cdot \nabla h=J(\psi ,h)$. The full magnetic flux function, including the imposed field, must be retained in the magnetic force; using the perturbation alone would lose its linear restoring term.

The scalar vorticity is $(\nabla \times \mathbf{u}{)}_y=-{\nabla }^2\psi$. Also $(\nabla \times \mathbf{B}{)}_y=-{\nabla }^{2}A$, and henceThe buoyancy perturbation is $g{\alpha }_T(T-T_0)\hat{\mathbf{z}}$, whose curl in the $y$ direction is $-g{\alpha }_T(T-T_0{)}_x$. Taking the curl of the momentum equation and multiplying by minus one eliminates pressure and therefore givesWe have restored ${\mu }_0$ in this dimensional expression. The printed magnetic force uses units in which ${\mu }_0=1$, or equivalently a correspondingly rescaled field.

Use length scale $d$, time scale $d^2/\kappa$, velocity scale $\kappa /d$, stream-function scale $\kappa$, magnetic flux-function scale $B_{0}d$, and temperature perturbation scale $\Delta T$. Thus the dimensionless total flux function isHere $x,z$ now denote coordinates divided by $d$, and the same $T$ notation in the second identity denotes the physical temperature. The dimensionless parameters for thermal-diffusion scaling of planar magnetoconvection areThese are the Rayleigh number, Chandrasekhar number, Prandtl number and magnetic-to-thermal diffusivity ratio. The nondimensional magnetic-force coefficient is $B_0^2{d}^2/({\mu }_0{\rho }_r{\kappa }^2)=\sigma \zeta Q$, so omitting the factor $\zeta$ would use a different magnetic parameter.

The resistive induction equation becomes $A_t+J(\psi ,A)=\zeta {\nabla }^{2}A$. Since $J(\psi ,x)=-{\psi }_z$, its perturbation equation has source $+{\psi }_z$. Similarly, advection of the conductive temperature gradient gives $J(\psi ,-z)=-{\psi }_x$, producing the source $+{\psi }_x$ in the perturbation heat equation. The resulting nonlinear equations are thereforeThis derives both forcing signs from the background fields, rather than selecting them by a stream-function convention after the fact.

For the specified roll mode put $K=\pi /\lambda$ and $p_L=K^2+{\pi }^2$, the positive eigenvalue of minus the Laplacian on each spatial factor. Writeand take $\psi =(p_L/K)a{f}_s$, $\Theta =b{f}_T$, $\chi =\lambda c{f}_A$, with amplitudes depending on $\tau =p_{L}t$. These factors satisfy the printed boundary conditions: sine factors make the normal velocity and $B_x=-{\chi }_z$ vanish at the required faces; the second derivatives of the velocity factors give the stress-free conditions; $\Theta$ vanishes at the top and bottom and ${\Theta }_x$ vanishes at the sides.

Discard quadratic perturbation terms. Since ${\nabla }^{2}A={\nabla }^2\chi$, the magnetic force linearizes to $J(x,{\nabla }^2\chi )=({\nabla }^2\chi {)}_z$. The needed derivatives areThe heat and induction equations give $b^{\prime }=a-b$ and $c^{\prime }=a-\zeta c$. In the vorticity equation, its left side is $-(p_L^3/K)a^{\prime }{f}_s$ and its three right-side contributions are $\sigma [(p_L^3/K)a-RKb+\zeta Q{p}_L\pi \lambda c]f_s$. Consequently, withthe three-amplitude vertical-field magnetoconvection system isPrimes now mean $d/d\tau$. They are genuine time derivatives, as the original PDF shows.

For its linear stability, seek amplitudes proportional to $e^{s\tau }$. Eliminating $b,c$ or taking the determinant giveswhereAssume the physical range $\sigma >0$, $\zeta >0$, $q\ge 0$. The Routh-Hurwitz stability criterion gives strict decay exactly when $A_2>0$, $A_3>0$ and $A_1{A}_2>A_3$. At $r=0$ these hold. As $r$ increases, the first loss of stability can occur through a zero eigenvalue or a conjugate imaginary pair.

The stationary threshold isAt this value, $P(s)=s(s^2+A_{1}s+C)$ withIf $C>0$, the other two roots have negative real parts and the zero root is simple. Its derivative with respect to $r$ is $ds/dr=\sigma \zeta /C>0$. This is stationary onset; the reflection symmetry of the roll permits a generic pitchfork bifurcation. The linear calculation does not determine whether the nonlinear branch is supercritical or subcritical.

For oscillatory onset set $s=i\omega$, $\omega \ne 0$. Separating real and imaginary parts gives ${\omega }^2=A_2$ and $A_1{A}_2=A_3$. Solving the latter for $r$ yieldsA genuine Hopf bifurcation onset requires ${\omega }_H^2>0$, soAt this threshold $P(s)=(s+A_1)(s^2+{\omega }_H^2)$, giving the third root $-A_1<0$. The pair crosses transversely: differentiating $P(s,r)=0$ at $i{\omega }_H$ gives

The first instability of the conductive state is therefore stationary at $r_s$ if $\zeta \ge 1$, or if $0<\zeta <1$ and $q<q_{\ast }$. It is oscillatory at $r_H<r_s$ if $0<\zeta <1$ and $q>q_{\ast }$. At $q=q_{\ast }$, the two thresholds meet and $P(s)=s^2(s+A_1)$: the frequency is zero and neither a simple pitchfork nor a nonzero-frequency Hopf test applies. The oscillatory marginality in vertical-field magnetoconvection formula must not be used where its frequency squared is negative.

For conversion back to physical parameters, $R_{s,H}={\lambda }^2{p}_L^3{r}_{s,H}/{\pi }^2$, while the dimensional Hopf frequency is $(\kappa p_L/d^2){\omega }_H$. These are onset conditions for the specified spatial mode. Identifying the globally first mode would additionally require comparison over the allowed spatial modes. Nonlinear saturation and the stability of the bifurcating finite-amplitude states are not determined by this linear three-amplitude system alone.