Spectral algebra and contour projections

This companion supplies the finite-dimensional complex algebra used in Quadratic Hamilton maps and positive complex planes. All spaces here are finite dimensional. No diagonalizability assumption is made for a general complex matrix.

Original programme proofs and illustration: GPT-6 Astra (OpenAI), Ultra, 5 October 2026; CC0. Earlier components retain their own notices.

A0. Exact earlier inputs

U001 Q5 proves real orthogonal diagonalization, including singular forms. M0a proves the invariance and additivity of inertia. C0 supplies basis extension, rank and annihilator dimensions. The U001 finite-calculus proofs supply matrix inversion and the inverse theorem. Its finite algebra and compactness proofs and complex exponential and circle proofs give determinant identities, compact extrema, convergent geometric sums, complex arithmetic, all circle angles and differentiation of exponentials. Exact entries are recorded in the proof map.

Gaussian elimination works over the complex field just as over the real field: choose a nonzero pivot, divide its row by that pivot and subtract multiples from the other rows. The remaining rectangle has one fewer row and column. Induction constructs a basis of the kernel by assigning its free coordinates, and pivot columns give a basis of the image. In particular a square matrix has nonzero determinant exactly when it is invertible, by the determinant row operations and the cofactor inverse. The complex versions of rank-nullity and basis extension used below follow from these constructions.

A1. Complex polynomial roots and factorization

Every nonconstant complex polynomial has a root and factors completely into linear factors with their multiplicities.

Proof. If its degree is dd, its leading term and the triangle inequality give ∣p(z)∣→∞|p(z)|\to\infty as ∣z∣→∞|z|\to\infty: for ∣z∣≥1|z|\ge1, the sum of all lower terms is at most C∣z∣d−1C|z|^{d-1}, whereas the leading term has size ∣ad∣∣z∣d|a_d||z|^d. Thus ∣p∣|p| attains a global minimum at some z0z_0, by compactness of a sufficiently large closed disk.

Suppose p(z0)≠0p(z_0)\ne0. Expand the finite polynomial at that point:

p(z0+w)p(z0)=1+awk+∑j=k+1dbjwj,a≠0,k≥1.(A1) \frac{p(z_0+w)}{p(z_0)} =1+a w^k+\sum_{j=k+1}^d b_jw^j,\qquad a\ne0,\quad k\ge1. \tag{A1}

Such a first nonzero coefficient exists, since translation and division by a nonzero constant do not make a nonconstant polynomial constant. The circle-angle theorem P16.3 supplies a real θ\theta for which aeikθ=−∣a∣a e^{ik\theta}=-|a|. Explicitly, write a/∣a∣=eiϕa/|a|=e^{i\phi} and take θ=(π−ϕ)/k\theta=(\pi-\phi)/k. For w=reiθw=re^{i\theta}, 0<r≤10<r\le1, the tail has size at most Crk+1Cr^{k+1}, with C=∑j>k∣bj∣C=\sum_{j>k}|b_j|. Choose rr so small that ∣a∣rk<1|a|r^k<1 and Cr<∣a∣Cr<|a|. Then

∣p(z0+w)p(z0)∣≤1−∣a∣rk+Crk+1<1,(A2) \left|\frac{p(z_0+w)}{p(z_0)}\right| \le 1-|a|r^k+Cr^{k+1}<1, \tag{A2}

contradicting the minimum. Hence p(z0)=0p(z_0)=0.

Polynomial division by z−z0z-z_0 gives a polynomial of degree d−1d-1 and a constant remainder equal to p(z0)p(z_0). This division is obtained by subtracting the leading multiple repeatedly, so each step reduces the degree. The remainder is zero. Induction proves complete factorization. The same division proves that a nonzero degree-dd polynomial has at most dd distinct roots. □\square

A2. The characteristic identity

For a complex dd-by-dd matrix TT, its characteristic polynomial p(z)=det⁡(zI−T)p(z)=\det(zI-T) satisfies p(T)=0p(T)=0.

Proof. The dimension-zero case is immediate. For d≥1d\ge1, cofactor expansion gives the polynomial matrix identity

(zI−T)adj⁡(zI−T)=p(z)I.(A3) (zI-T)\operatorname{adj}(zI-T)=p(z)I. \tag{A3}

Write the adjugate as ∑j=0d−1Ajzj\sum_{j=0}^{d-1}A_jz^j and p(z)=zd+∑j=0d−1cjzjp(z)=z^d+\sum_{j=0}^{d-1}c_jz^j. Comparing coefficients gives

Ad−1=I,Aj−1−TAj=cjI (1≤j≤d−1),−TA0=c0I.(A4) A_{d-1}=I,\qquad A_{j-1}-TA_j=c_jI\ (1\le j\le d-1), \qquad -TA_0=c_0I. \tag{A4}

Successive substitution expresses A0A_0 as Td−1+cd−1Td−2+⋯+c1IT^{d-1}+c_{d-1}T^{d-2}+\cdots+c_1I. The last equation is p(T)=0p(T)=0. This coefficient argument avoids substituting a matrix into a polynomial identity whose other coefficients might not commute with it. □\square

A3. The full generalized spectral splitting

Let the distinct roots of pp be λ1,…,λs\lambda_1,\ldots,\lambda_s, with multiplicities mim_i. Then

E=⨁i=1sVi,Vi=ker⁡(T−λiI)mi=ker⁡(T−λiI)d.(A5) E=\bigoplus_{i=1}^s V_i,\qquad V_i=\ker(T-\lambda_i I)^{m_i} =\ker(T-\lambda_i I)^d. \tag{A5}

Each summand is TT-invariant, and T=λiI+NiT=\lambda_i I+N_i on it, with Nimi=0N_i^{m_i}=0.

Proof. Put fi(z)=(z−λi)mif_i(z)=(z-\lambda_i)^{m_i} and qi=p/fiq_i=p/f_i. There are polynomials ui,viu_i,v_i with uiqi+vifi=1u_iq_i+v_if_i=1. Indeed polynomial long division gives the Euclidean algorithm; successive nonzero remainders have decreasing degree. Its last nonzero remainder divides both polynomials. By A1, a nonconstant common divisor would have a common root, which these two factors do not have. The remainder is a nonzero constant; reverse the divisions and divide by that constant to obtain the asserted identity.

Set Pi=ui(T)qi(T)P_i=u_i(T)q_i(T). A2 gives fi(T)Pi=0f_i(T)P_i=0, so its image lies in ViV_i. On ViV_i, the polynomial identity gives Pi=IP_i=I; on VjV_j, j≠ij\ne i, it gives Pi=0P_i=0 because qiq_i contains the factor fjf_j. Also ∑iuiqi−1\sum_i u_iq_i-1 is divisible by every fif_i: reduce modulo fif_i and use the same identity. Pairwise coprimality implies divisibility by their product. For two factors, if a∣ra\mid r, b∣rb\mid r, and ua+vb=1ua+vb=1, writing r=akr=ak gives k=uak+vbkk=uak+vbk, whose two terms are divisible by bb; hence ab∣rab\mid r. Induct over the factors. Consequently ∑iPi=I\sum_iP_i=I by A2. The previously checked restrictions prove that this is a direct sum and that the PiP_i are its projections.

If μ≠λi\mu\ne\lambda_i, then on ViV_i

(T−μI)−1=∑j=0mi−1(−Ni)j(λi−μ)j+1.(A6) (T-\mu I)^{-1} =\sum_{j=0}^{m_i-1} \frac{(-N_i)^j}{(\lambda_i-\mu)^{j+1}}. \tag{A6}

Multiplication telescopes, proving the formula. Thus the ddth kernel in (A5) has no component in any other summand, and it equals ViV_i. Invariance follows because all the operators used are polynomials in TT. If TT is real, conjugation in (A5) sends VλV_\lambda to VλˉV_{\bar\lambda}. □\square

A4. Nilpotent chains and their invariants

Every nilpotent endomorphism has a basis consisting of finite chains v,Nv,…,Nm−1vv,Nv,\ldots,N^{m-1}v. The number of chains is dim⁡ker⁡N\dim\ker N; their lengths are determined by the dimensions of the kernels of all powers. Applying this on the summands in A3 proves the full Jordan decomposition, over C\mathbb C. For a real nilpotent map, the entire construction is real.

Proof. If the space is nonzero, let mm be the smallest integer with Nm=0N^m=0, and choose vv with Nm−1v≠0N^{m-1}v\ne0. Its mm chain vectors are independent: in a nontrivial linear relation choose the smallest power jj with nonzero coefficient, then apply Nm−1−jN^{m-1-j}. Only that coefficient times Nm−1vN^{m-1}v survives, a contradiction.

Extend these vectors to a basis and choose a linear functional ℓ\ell equal to one on Nm−1vN^{m-1}v and zero on all the earlier chain vectors. Define

Px=∑j=0m−1ℓ(Nm−1−jx) Njv.(A7) P x=\sum_{j=0}^{m-1}\ell(N^{m-1-j}x)\,N^jv. \tag{A7}

On NkvN^kv the only nonzero coefficient is the one with j=kj=k; powers at least mm vanish. Thus PP is the identity on the chain span CC, has image CC, and P2=PP^2=P. In PNxPNx its j=0j=0 coefficient vanishes because Nm=0N^m=0. For j≥1j\ge1, its coefficient at NjvN^jv is ℓ(Nm−jx)\ell(N^{m-j}x), exactly as in NPxNPx. Hence PN=NPPN=NP. It follows that E=C⊕ker⁡PE=C\oplus\ker P is an invariant splitting. The latter space has smaller dimension and its restriction is nilpotent. Induction completes the chain basis, including m=1m=1.

A length-mm chain contributes min⁡(j,m)\min(j,m) to dim⁡ker⁡Nj\dim\ker N^j. Therefore

dim⁡ker⁡Nj−dim⁡ker⁡Nj−1=#{chains of length at least j}.(A8) \dim\ker N^j-\dim\ker N^{j-1} =\#\{\text{chains of length at least }j\}. \tag{A8}

These differences determine the number of each exact length. At j=1j=1 they count all chains. In particular, a nonzero nilpotent space with a one-dimensional kernel consists of one chain. The matrix of T=λiI+NiT=\lambda_i I+N_i in its chain basis has diagonal entries λi\lambda_i; hence the characteristic polynomial on that summand is (z−λi)dim⁡Vi(z-\lambda_i)^{\dim V_i}. Comparison of the factors in A3 gives dim⁡Vi=mi\dim V_i=m_i. Thus all multiplicities and Jordan lengths used in the lesson are actual conjugacy invariants. □\square

A5. Circle moments and the exact resolvent projection

Let γ(θ)=c+Reiθ\gamma(\theta)=c+Re^{i\theta}, 0≤θ≤2π0\le\theta\le2\pi, with R>0R>0, traversed counterclockwise. For λ\lambda off the circle,

12πi∮γdzz−λ={1,∣λ−c∣<R,0,∣λ−c∣>R,∮γdz(z−λ)j+1=0(j≥1).(A9) \frac1{2\pi i}\oint_\gamma\frac{dz}{z-\lambda} =\begin{cases}1,&|\lambda-c|<R,\\0,&|\lambda-c|>R,\end{cases} \qquad \oint_\gamma\frac{dz}{(z-\lambda)^{j+1}}=0\quad(j\ge1). \tag{A9}

Proof. In the inside case put a=(λ−c)/Ra=(\lambda-c)/R. Substituting dz=iReiθdθdz=iRe^{i\theta}d\theta gives i/(1−ae−iθ)i/(1-ae^{-i\theta}). Its geometric series converges uniformly because ∣a∣<1|a|<1. The constant term integrates to 2πi2\pi i; every other term integrates to zero, by ∫02πeikθdθ=0\int_0^{2\pi}e^{ik\theta}d\theta=0 for a nonzero integer kk, which follows from differentiation and the period 2π2\pi. Uniform tails justify integration term by term. In the outside case expand

1c−λ+Reiθ=1c−λ∑k≥0(−Rc−λ)keikθ.(A10) \frac1{c-\lambda+Re^{i\theta}} =\frac1{c-\lambda} \sum_{k\ge0}\left(-\frac R{c-\lambda}\right)^ke^{ik\theta}. \tag{A10}

After multiplying by iReiθiRe^{i\theta}, every frequency is a positive integer, so the integral is zero. Again the series is uniform. For j≥1j\ge1, the integrand has the single-valued primitive −(z−λ)−j/j-(z-\lambda)^{-j}/j along the circle. The real chain rule and the fundamental theorem give zero because its endpoint values agree. This proves (A9) without a residue theorem.

For a matrix TT, restrict to a generalized summand of A3. There the finite identity

(zI−T)−1=∑j=0mi−1Nij(z−λi)j+1(A11) (zI-T)^{-1} =\sum_{j=0}^{m_i-1}\frac{N_i^j}{(z-\lambda_i)^{j+1}} \tag{A11}

follows by multiplication, using nilpotence. Applying (A9) shows that the integral (2πi)−1∮γ(zI−T)−1dz(2\pi i)^{-1}\oint_\gamma(zI-T)^{-1}dz is the identity on every inside summand and zero on every outside summand. It is exactly the projection onto the full inside generalized spectral sum and commutes with TT. In particular this is valid for nontrivial Jordan blocks. □\square

A6. A common contour for a continuous matrix family

Suppose TtT_t is a continuous complex matrix family on 0≤t≤10\le t\le1, and every TtT_t has no real eigenvalue. There are M>0,δ>0M>0,\delta>0 for which every spectral value satisfies

∣λ∣≤M,∣Im⁡λ∣≥δ.(A12) |\lambda|\le M,\qquad |\operatorname{Im}\lambda|\ge\delta. \tag{A12}

One fixed upper-half-plane circle encloses all upper spectra and no lower spectra. The associated projections are continuous, with locally constant rank and locally continuous bases of their ranges.

Proof. The Euclidean norm estimate ∥T∥≤dmax⁡jk∣Tjk∣\|T\|\le d\max_{jk}|T_{jk}| follows from scalar Cauchy–Schwarz. A continuous finite collection of entries is bounded on the parameter interval; choose M>0M>0 bounding these operator norms. Every eigenvalue has an eigenvector by A0, so ∣λ∣≤∥Tt∥≤M|\lambda|\le\|T_t\|\le M.

If there were no positive δ\delta, take parameters tjt_j and eigenvalues λj\lambda_j with imaginary parts tending to zero. Compactness gives a subsequence with (tj,λj)→(t,λ)(t_j,\lambda_j)\to(t,\lambda), where λ\lambda is real. The determinant is a polynomial in its entries, so det⁡(λI−Tt)=lim⁡jdet⁡(λjI−Ttj)=0\det(\lambda I-T_t)=\lim_j\det(\lambda_jI-T_{t_j})=0. This is a forbidden real eigenvalue. Reduce δ\delta, if necessary, so 0<δ≤M0<\delta\le M.

Put

L=M2/δ+δ,R=L−δ/2,γ(θ)=iL+Reiθ.(A13) L=M^2/\delta+\delta,\qquad R=L-\delta/2,\qquad \gamma(\theta)=iL+Re^{i\theta}. \tag{A13}

The circle and its interior lie above the line Im⁡z=δ/2\operatorname{Im}z=\delta/2. For an upper spectral value,

∣λ−iL∣2≤M2+L2−2Lδ<L2−Lδ+δ2/4=R2.(A14) |\lambda-iL|^2 \le M^2+L^2-2L\delta <L^2-L\delta+\delta^2/4=R^2. \tag{A14}

The strict inequality holds since Lδ=M2+δ2L\delta=M^2+\delta^2. Thus all upper values are inside, while all lower values are outside. There are no spectral values on the circle.

The cofactor formula for the inverse gives a continuous resolvent on the compact set of tt and θ\theta; its determinant denominator is bounded away from zero. Uniform continuity and the finite contour length therefore make its integral continuous in tt, in matrix norm. A5 proves each integral is the required projection.

For two projections P,QP,Q with ∥P−Q∥<1\|P-Q\|<1, the restriction of QQ to ran⁡P\operatorname{ran}P is injective: if Px=xPx=x and Qx=0Qx=0, then ∥x∥≤∥P−Q∥∥x∥\|x\|\le\|P-Q\|\|x\|, so x=0x=0. Exchanging the projections gives equal ranks. For a basis vjv_j of ran⁡P\operatorname{ran}P, the vectors QvjQv_j therefore form a basis of ran⁡Q\operatorname{ran}Q. They vary continuously with QQ. This gives the asserted local constant ranks and continuous bases. □\square

A fixed upper spectral contour and the uniformly separated admissible spectral regions

Open the full-size figure.

Figure A1. The exact illustrative constants are M=2,δ=1M=2,\delta=1, so (A13) gives center 5i5i and radius 9/29/2. The right panel magnifies the two regions ∣z∣≤2|z|\le2, ∣Im⁡z∣≥1|\operatorname{Im}z|\ge1 where (A12) allows spectral values. The solid contour encloses the entire upper region; its lowest point has imaginary part 1/21/2. The arrows give the counterclockwise orientation in (A9). No individual eigenvalues or diagonalizability are presumed. Equation (A14), rather than the drawing, proves the inclusion.

A7. Semidefinite Hermitian forms and openness

Use the convention that a Hermitian form hh is linear in its first argument. If h≥0h\ge0, then

∣h(x,y)∣2≤h(x,x)h(y,y).(A15) |h(x,y)|^2\le h(x,x)h(y,y). \tag{A15}

In particular a null vector pairs to zero with every vector.

Proof. If h(y,y)=c>0h(y,y)=c>0, set b=h(x,y)b=h(x,y) and expand 0≤h(x−(b/c)y,x−(b/c)y)=h(x,x)−∣b∣2/c0\le h(x-(b/c)y,x-(b/c)y)=h(x,x)-|b|^2/c. If h(y,y)=0h(y,y)=0, nonnegativity of h(y+tx,y+tx)h(y+tx,y+tx) for all complex tt forces h(x,y)=0h(x,y)=0: otherwise choose the phase of a sufficiently small tt to make its real linear term negative, dominating the quadratic term. This proves (A15).

A positive definite Hermitian matrix G0G_0 has a positive minimum aa on the complex Euclidean unit sphere, regarded as a compact real sphere. If ∥G−G0∥<a\|G-G_0\|<a, then vˉTGv≥(a−∥G−G0∥)∣v∣2>0\bar v^TGv\ge(a-\|G-G_0\|)|v|^2>0 for v≠0v\ne0. Thus strict positivity is open. For a continuous family of projections of fixed rank, apply this to the Gram matrices of the continuous bases in A6.

For later use, a nonempty subset of [0,1][0,1] that is both relatively open and closed is the whole interval. If not, choose one point in it and one outside. After reversing the interval if needed, the first is left of the second. The supremum of its points between them belongs to it by closedness and is less than the second point. Openness then gives a still larger point in it, a contradiction. □\square

A8. Positive square roots and orthonormal bases

For a positive definite real symmetric matrix AA, Q5 gives an orthogonal UU and positive aja_j with A=Udiag⁡(aj)UTA=U\operatorname{diag}(a_j)U^T. Therefore

D=Udiag⁡(aj)UT(A16) D=U\operatorname{diag}(\sqrt{a_j})U^T \tag{A16}

is real symmetric positive definite, invertible, and satisfies D2=AD^2=A. The scalar positive roots and their signs are among the U001 inputs. This proves the square-root construction used in the graph normalization.

It is the unique positive symmetric square root. If EE is another one, EA=EE2=E2E=AEEA=EE^2=E^2E=AE, so EE preserves each eigenspace of AA. Its symmetric restriction there has an orthonormal eigenbasis by Q5. On the eigenspace with eigenvalue a>0a>0, every eigenvalue of that restriction is positive and has square aa, so it equals a\sqrt a. Hence E=DE=D.

For any positive real bilinear inner product gg, a real basis v1,…,vkv_1,\ldots,v_k can be made gg-orthonormal. After e1,…,ej−1e_1,\ldots,e_{j-1} have been chosen, set

wj=vj−∑l<jg(vj,el)el,ej=wj/g(wj,wj).(A17) w_j=v_j-\sum_{l<j}g(v_j,e_l)e_l,\qquad e_j=w_j/\sqrt{g(w_j,w_j)}. \tag{A17}

The earlier orthonormality makes every g(wj,el)g(w_j,e_l) zero. Independence of the original basis implies wj≠0w_j\ne0, so its denominator is positive. This induction proves all claims, also on a given subspace with the restricted product.

A9. Linear matrix flows

For every finite matrix TT, define

etT=∑j=0∞tjTjj!.(A18) e^{tT}=\sum_{j=0}^\infty\frac{t^jT^j}{j!}. \tag{A18}

The inequality ∥AB∥≤∥A∥∥B∥\|AB\|\le\|A\|\|B\| follows immediately from the operator norm definition. Thus on ∣t∣≤a|t|\le a the series and each derivative series are dominated by the convergent scalar exponential series with argument a∥T∥a\|T\|, multiplied by a fixed power of ∥T∥\|T\|. The uniform derivative-limit argument of U001 P15.1 gives (etT)′=TetT(e^{tT})'=Te^{tT} and initial value II.

Multiplication of two absolutely convergent series, and the finite binomial formula, give esTetT=e(s+t)Te^{sT}e^{tT}=e^{(s+t)T}. Alternatively both sides as functions of ss solve the same finite linear initial-value problem. Hence etTe^{tT} is invertible, with inverse e−tTe^{-tT}, and gives the entire linear flow. If Tm=0T^m=0, (A18) terminates at m−1m-1. For an oscillator T2=−IT^2=-I, its even and odd terms are cos⁡t I+sin⁡t T\cos t\,I+\sin t\,T, with the trigonometric functions proved in P15–P16. The hyperbolic diagonal flow is obtained by applying the scalar exponential on each diagonal entry.

If TTJ+JT=0T^TJ+JT=0, differentiate (etT)TJetT(e^{tT})^TJ e^{tT}. Its derivative is zero and its initial value is JJ; thus the full flow is symplectic. These arguments justify the flow notation and time normalization in every example and exercise of the lesson.

Scope and credit

The companion provides the elementary algebra and contour proofs needed for the Hörmander III Section 21.5 treatment used by the main lesson. It is independently written; no book text or figure is reproduced. It supplies full arguments in addition to the main lesson's mathematical source citation. The figure source and font notice are retained. The proof map connects every earlier input.