Generalized Eigenspace Decomposition

Not every operator on a complex vector space can be diagonalized. So a natural question arises. If eigenvectors alone cannot span the space, what is the correct substitute? The answer is to enlarge each eigenspace to a generalized eigenspace. This consists of vectors that eigenvectors annihilate only after repeated application of a shifted operator. This single adjustment repairs the theory completely. Over the complex numbers, every operator splits the whole space into a direct sum of these generalized eigenspaces. This result is the structural heart of eigentheory for operators that fail to be diagonalizable. It explains precisely why diagonalization can fail. Nontrivial nilpotent parts get trapped inside generalized eigenspaces. Yet it still delivers a clean, canonical decomposition of the ambient space. The theorem converts an arbitrary operator into a block-diagonal object. Each block is "an eigenvalue plus something nilpotent." Nilpotent operators are themselves completely classified by their Jordan block structure. The big picture is that complex linear algebra has two layers of structure. These are the semisimple layer (eigenvalues) and the nilpotent layer. The nilpotent layer measures how far generalized eigenvectors are from being honest eigenvectors. The Generalized Eigenspace Decomposition Theorem is the bridge connecting these layers. It is the direct predecessor to the Jordan Canonical Form. This refines each generalized eigenspace into an explicit basis of Jordan chains. This article gives the precise definitions. It states and proves the decomposition theorem in full, using the Cayley-Hamilton Theorem and properties of Generalized Eigenvectors. It works through two genuinely non-diagonalizable examples in detail. It also shows exactly which hypothesis cannot be dropped: algebraic closedness of the field.

Key takeaways
  • A generalized eigenvector for eigenvalue λ is killed by (T−λI)k(T-\lambda I)^k for some k≥1k\ge 1, not necessarily k=1k=1.
  • The generalized eigenspace G(λ,T)G(\lambda,T) is the union (equivalently, for large enough kk, the single subspace) of these kernels.
  • Over C\mathbb{C}, every operator's generalized eigenspaces span the whole space and intersect trivially: V=G(λ1,T)⊕⋯⊕G(λm,T)V = G(\lambda_1,T)\oplus\cdots\oplus G(\lambda_m,T).
  • The dimension of each generalized eigenspace equals the algebraic multiplicity of λ in the characteristic polynomial.
  • Each G(λ,T)G(\lambda,T) is TT-invariant, and TT restricted there equals λI\lambda I plus a nilpotent operator.
  • Key idea: enlarging eigenspaces to generalized eigenspaces repairs the failure of diagonalizability, and does so completely over an algebraically closed field.

Where this lives today

The generalized eigenspace decomposition underlies the computation of matrix exponentials etAe^{tA} in systems of linear ODEs: writing A=∑i(λiPi+Ni)A = \sum_i (\lambda_i P_i + N_i) on each block lets one compute etAe^{tA} explicitly as ∑ieλit(I+tNi+⋯+tdi−1Nidi−1/(di−1)!)Pi\sum_i e^{\lambda_i t}(I + tN_i + \cdots + t^{d_i-1}N_i^{d_i-1}/(d_i-1)!)P_i, which is exactly how engineers analyze stability of linear control systems. In quantum mechanics and more broadly operator theory, the decomposition is the finite-dimensional prototype of spectral theory, generalized later to the Jordan Canonical Form for a fully explicit block structure, and further to the spectral theorem for normal operators when extra structure (self-adjointness, unitarity) is present. In numerical linear algebra, the sensitivity of eigenvalues under repeated roots — a delicate issue in floating-point computation — is precisely governed by the size of the Jordan blocks inside each generalized eigenspace. In algebraic combinatorics and representation theory, decomposing a representation's underlying operator into generalized eigenspaces is a first step toward understanding module structure over polynomial rings, tying directly into the structure theorem for finitely generated modules over a PID.

Ask the tutor: How do I find an explicit basis of Jordan chains inside a generalized eigenspace once I know its dimension?

DefinitionGeneralized eigenvector and generalized eigenspace

Let VV be a finite-dimensional vector space over a field F\mathbb{F}, and let T:V→VT: V \to V be a linear operator. Let λ∈F\lambda \in \mathbb{F}. A nonzero vector v∈Vv \in V is a generalized eigenvector of TT corresponding to λ\lambda if

(T−λI)kv=0(T - \lambda I)^k v = 0

for some positive integer kk. (If k=1k=1 works, vv is an ordinary eigenvector.) The generalized eigenspace of TT corresponding to λ\lambda is

G(λ,T)={v∈V:(T−λI)kv=0 for some k≥1}=⋃k=1∞ker⁡(T−λI)k.G(\lambda, T) = \{v \in V : (T-\lambda I)^k v = 0 \text{ for some } k \ge 1\} = \bigcup_{k=1}^{\infty} \ker (T-\lambda I)^k.

Since ker⁡(T−λI)k⊆ker⁡(T−λI)k+1\ker(T-\lambda I)^k \subseteq \ker(T-\lambda I)^{k+1} for every kk (if (T−λI)kv=0(T-\lambda I)^k v = 0 then (T−λI)k+1v=0(T-\lambda I)^{k+1}v=0), and dim⁡V<∞\dim V < \infty, this increasing chain of subspaces stabilizes. In fact it stabilizes by k=n:=dim⁡Vk = n := \dim V, so

G(λ,T)=ker⁡(T−λI)n.G(\lambda, T) = \ker (T - \lambda I)^n .

In particular G(λ,T)G(\lambda,T) is a subspace of VV, and it is nonzero precisely when λ\lambda is an eigenvalue of TT.

Intuition

Think of an eigenvalue λ\lambda as a "resonant frequency" of TT: ordinary eigenvectors are directions along which TT acts by pure scaling. When TT is not diagonalizable, some directions are almost eigenvectors — TT moves them to λ\lambda times themselves plus a small correction, and that correction itself gets killed by a further application of T−λIT - \lambda I. A generalized eigenvector of "rank kk" is a vector requiring exactly kk applications of T−λIT-\lambda I to reach zero; feeding it through T−λIT - \lambda I repeatedly produces a chain

vk↦vk−1↦⋯↦v1↦0,v_k \mapsto v_{k-1} \mapsto \cdots \mapsto v_1 \mapsto 0,

where each viv_i is a genuine eigenvector once i=1i=1. This chain is precisely the seed of a Jordan block. The generalized eigenspace G(λ,T)G(\lambda,T) collects all vectors belonging to some such chain for the given λ\lambda; it is a whole tower built on top of the ordinary eigenspace. The decomposition theorem says these towers, one for each distinct eigenvalue, use up the entire space with no overlap and no leftover, provided the field is algebraically closed (so that the characteristic polynomial factors completely and every root is "found").

Figure
A rank-3 Jordan chain feeding into the eigenspace, and the resulting stabilized kernel chain ker⁡(T−λI)k\ker(T-\lambda I)^k
Jordan chain for eigenvalue λ (rank 3) v₃ v₂ v₁ 0 T−λI T−λI T−λI Stabilizing kernel chain ker(T−λI)¹ ker(T−λI)² ker(T−λI)³ = G(λ,T) stable for k ≥ 3
TheoremGeneralized Eigenspace Decomposition

Let VV be a finite-dimensional vector space over C\mathbb{C} with dim⁡V=n≥1\dim V = n \ge 1, and let T:V→VT: V \to V be a linear operator. Let λ1,…,λm\lambda_1, \dots, \lambda_m be the distinct eigenvalues of TT. Then: 1. (Invariance) Each G(λi,T)G(\lambda_i, T) is a TT-invariant subspace of VV. 2. (Direct sum decomposition)

V=G(λ1,T)⊕G(λ2,T)⊕⋯⊕G(λm,T).V = G(\lambda_1, T) \oplus G(\lambda_2, T) \oplus \cdots \oplus G(\lambda_m, T).

3. (Dimension formula) If did_i is the multiplicity of λi\lambda_i as a root of the characteristic polynomial χT(z)=det⁡(zI−T)\chi_T(z) = \det(zI - T), then

dim⁡G(λi,T)=difor each i,\dim G(\lambda_i, T) = d_i \qquad \text{for each } i,

and consequently G(λi,T)=ker⁡(T−λiI)diG(\lambda_i,T) = \ker (T-\lambda_i I)^{d_i}. 4. (Structure of the pieces) For each ii, the restriction T∣G(λi,T)T|_{G(\lambda_i,T)} equals λiI+Ni\lambda_i I + N_i where NiN_i is a nilpotent operator on G(λi,T)G(\lambda_i,T).

Proof

We use the following named lemmas, each proved from scratch, and the technique of primary decomposition via coprime polynomial factors of the characteristic (equivalently minimal) polynomial, combined with Cayley–Hamilton.

Step 0: Setup via Cayley–Hamilton. By the Cayley-Hamilton Theorem, the characteristic polynomial χT(z)=det⁡(zI−T)\chi_T(z) = \det(zI-T) satisfies χT(T)=0\chi_T(T) = 0. Since C\mathbb{C} is algebraically closed, χT\chi_T factors completely:

χT(z)=∏i=1m(z−λi)di,∑i=1mdi=n,\chi_T(z) = \prod_{i=1}^m (z-\lambda_i)^{d_i}, \qquad \sum_{i=1}^m d_i = n,

where λ1,…,λm\lambda_1,\dots,\lambda_m are the distinct roots (eigenvalues) and di≥1d_i \ge 1 their multiplicities. Since χT(T)=0\chi_T(T)=0, we have

∏i=1m(T−λiI)di=0as an operator on V.(∗)\prod_{i=1}^m (T-\lambda_i I)^{d_i} = 0 \quad \text{as an operator on } V. \tag{$\ast$}

Lemma A (kernels of coprime polynomials in TT split the space). Suppose p1(z),…,pm(z)p_1(z),\dots,p_m(z) are polynomials, pairwise coprime (no common root, since we work over C\mathbb C this means no shared linear factor), such that p1(T)p2(T)⋯pm(T)=0p_1(T)p_2(T)\cdots p_m(T) = 0. Let Wi=ker⁡pi(T)W_i = \ker p_i(T). Then V=W1⊕⋯⊕WmV = W_1 \oplus \cdots \oplus W_m. *Proof of Lemma A. Apply this with pi(z)=(z−λi)dip_i(z) = (z-\lambda_i)^{d_i}; these are pairwise coprime since the λi\lambda_i are distinct. We prove the general lemma by induction on mm. Base case m=1m=1: trivial, p1(T)=0p_1(T) = 0 forces W1=ker⁡p1(T)=VW_1 = \ker p_1(T) = V. Case m=2m=2:* Since p1,p2p_1,p_2 are coprime, by Bézout's identity for polynomials over the field C\mathbb C, there exist polynomials a(z),b(z)a(z), b(z) with

a(z)p1(z)+b(z)p2(z)=1.a(z)p_1(z) + b(z)p_2(z) = 1.

Substituting TT (all these are polynomials in the single operator TT, so they commute):

a(T)p1(T)+b(T)p2(T)=I.a(T)p_1(T) + b(T)p_2(T) = I.

For v∈Vv \in V, write

v=a(T)p1(T)v+b(T)p2(T)v=:v2+v1.v = a(T)p_1(T)v + b(T)p_2(T) v =: v_2 + v_1.

Since p1(T)p2(T)=0p_1(T)p_2(T) = 0 (as p1p2p_1p_2 divides the given product, or directly from (∗)(\ast) when m=2m=2), we get p2(T)v2=p2(T)a(T)p1(T)v=a(T)(p1(T)p2(T))v=0p_2(T) v_2 = p_2(T)a(T)p_1(T) v= a(T)\big(p_1(T)p_2(T)\big) v = 0, so v2∈ker⁡p2(T)=W2v_2 \in \ker p_2(T) = W_2. Similarly v1=b(T)p2(T)v∈ker⁡p1(T)=W1v_1 = b(T)p_2(T)v \in \ker p_1(T)=W_1 because p1(T)v1=b(T)p1(T)p2(T)v=0p_1(T)v_1 = b(T) p_1(T)p_2(T) v = 0. This shows V=W1+W2V = W_1 + W_2. For the sum to be direct, suppose w1+w2=0w_1 + w_2 = 0 with wi∈Wiw_i \in W_i. Applying a(T)p1(T)a(T)p_1(T) to 0=w1+w20 = w_1+w_2: a(T)p1(T)w1+a(T)p1(T)w2=0a(T)p_1(T)w_1 + a(T)p_1(T) w_2 = 0. But a(T)p1(T)w2=a(T)p1(T)w2a(T)p_1(T)w_2 = a(T)p_1(T) w_2; using a(T)p1(T)=I−b(T)p2(T)a(T)p_1(T) = I - b(T)p_2(T), we get a(T)p1(T)w2=w2−b(T)p2(T)w2=w2−0=w2a(T)p_1(T) w_2 = w_2 - b(T)p_2(T) w_2 = w_2 - 0 = w_2 since p2(T)w2=0p_2(T) w_2 = 0. Meanwhile a(T)p1(T)w1=0a(T)p_1(T) w_1 = 0 because p1(T)w1=0p_1(T) w_1 = 0. Hence the equation becomes 0+w2=00 + w_2 = 0, i.e. w2=0w_2 = 0, and then w1=0w_1 = 0 too. So W1∩W2={0}W_1 \cap W_2 = \{0\}, and V=W1⊕W2V = W_1 \oplus W_2. Inductive step: Suppose the lemma holds for m−1m-1 pairwise coprime polynomials. Given p1,…,pmp_1,\dots,p_m pairwise coprime with ∏pi(T)=0\prod p_i(T) = 0, set q(z)=∏i=2mpi(z)q(z) = \prod_{i=2}^m p_i(z). Since p1p_1 is coprime to each pip_i (i≥2i\ge 2), and coprimality of p1p_1 with each factor of qq implies coprimality of p1p_1 with qq (a standard property of coprime polynomials: if gcd⁡(p1,pi)=1\gcd(p_1,p_i)=1 for all ii, then gcd⁡(p1,∏pi)=1\gcd(p_1, \prod p_i) = 1, provable by Bézout multiplicatively), p1p_1 and qq are coprime. Also p1(T)q(T)=∏ipi(T)=0p_1(T) q(T) = \prod_i p_i(T) = 0. By the m=2m=2 case, V=ker⁡p1(T)⊕ker⁡q(T)=W1⊕ker⁡q(T)V = \ker p_1(T) \oplus \ker q(T) = W_1 \oplus \ker q(T). Now restrict attention to U:=ker⁡q(T)U := \ker q(T). We claim U=ker⁡p2(T)⊕⋯⊕ker⁡pm(T)U = \ker p_2(T) \oplus \cdots \oplus \ker p_m(T) inside UU, using the inductive hypothesis applied to T∣UT|_U (note UU is TT-invariant since TT commutes with q(T)q(T), so q(T)Tu=Tq(T)u=0q(T)Tu = Tq(T)u = 0 for u∈Uu \in U). We have q(T∣U)=0q(T|_U) = 0 by definition of UU, matching the hypothesis of the inductive claim for p2,…,pmp_2,\ldots,p_m pairwise coprime. Hence U=ker⁡(p2(T)∣U)⊕⋯⊕ker⁡(pm(T)∣U)U = \ker(p_2(T)|_U)\oplus\cdots\oplus\ker(p_m(T)|_U). Since for i≥2i \ge 2, ker⁡(pi(T)∣U)=ker⁡pi(T)∩U=ker⁡pi(T)\ker(p_i(T)|_U) = \ker p_i(T) \cap U = \ker p_i(T) — the last equality because ker⁡pi(T)⊆U=ker⁡q(T)\ker p_i(T) \subseteq U=\ker q(T) automatically (if pi(T)v=0p_i(T) v = 0 then q(T)v=0q(T) v = 0 too, since pi∣qp_i \mid q) — we conclude

U=ker⁡p2(T)⊕⋯⊕ker⁡pm(T)=W2⊕⋯⊕Wm.U = \ker p_2(T) \oplus \cdots \oplus \ker p_m(T) = W_2 \oplus \cdots \oplus W_m.

Combining, V=W1⊕U=W1⊕W2⊕⋯⊕WmV = W_1 \oplus U = W_1 \oplus W_2 \oplus \cdots \oplus W_m. This completes the induction and proves Lemma A. ■\blacksquare

Applying Lemma A. Take pi(z)=(z−λi)dip_i(z) = (z-\lambda_i)^{d_i} in (∗)(\ast). These are pairwise coprime (distinct linear factors, over C\mathbb C coprimality of powers of distinct linear polynomials is immediate: any common root would have to be a root of both z−λiz - \lambda_i and z−λjz-\lambda_j, impossible for λi≠λj\lambda_i \ne \lambda_j). Lemma A gives

V=ker⁡(T−λ1I)d1⊕⋯⊕ker⁡(T−λmI)dm.V = \ker(T-\lambda_1 I)^{d_1} \oplus \cdots \oplus \ker(T-\lambda_m I)^{d_m}.

Step 1: Identify ker⁡(T−λiI)di\ker(T-\lambda_i I)^{d_i} with G(λi,T)G(\lambda_i,T). By definition G(λi,T)=⋃kker⁡(T−λiI)kG(\lambda_i,T) = \bigcup_k \ker(T-\lambda_iI)^k, and this chain stabilizes at k=n=dim⁡Vk=n=\dim V at the latest (shown in the definition above). We must show it in fact stabilizes by k=dik = d_i, matching the exponent from Cayley–Hamilton, and that no more room is needed. First, ker⁡(T−λiI)di⊆G(λi,T)\ker(T-\lambda_i I)^{d_i} \subseteq G(\lambda_i,T) trivially, by definition of G(λi,T)G(\lambda_i,T) as a union including the k=dik=d_i term. Conversely, we show G(λi,T)⊆ker⁡(T−λiI)diG(\lambda_i,T) \subseteq \ker(T-\lambda_i I)^{d_i}, i.e. the chain of kernels for λi\lambda_i has already stabilized by step did_i. Suppose v∈ker⁡(T−λiI)kv \in \ker(T-\lambda_iI)^k for some k>dik > d_i; we show v∈ker⁡(T−λiI)div \in \ker(T-\lambda_i I)^{d_i}. Write v=∑jwjv = \sum_j w_j with wj∈G(λj,T)=ker⁡(T−λjI)djw_j \in G(\lambda_j,T) = \ker(T-\lambda_j I)^{d_j} using the direct sum decomposition just established (this uses the decomposition as a set-theoretic fact, not yet assuming the dimension count, so there is no circularity). Apply (T−λiI)k(T-\lambda_i I)^k:

0=(T−λiI)kv=∑j(T−λiI)kwj.0 = (T-\lambda_i I)^k v = \sum_j (T-\lambda_i I)^k w_j.

For j=ij = i: since wi∈ker⁡(T−λiI)diw_i \in \ker(T-\lambda_i I)^{d_i} and k>di≥dik > d_i \ge d_i, certainly (T−λiI)kwi=0(T-\lambda_i I)^k w_i = 0 as well (apply extra copies of T−λiIT - \lambda_i I to the already-zero vector (T−λiI)diwi(T-\lambda_i I)^{d_i} w_i). For j≠ij \neq i: we claim (T−λiI)(T - \lambda_i I) restricted to ker⁡(T−λjI)dj\ker(T-\lambda_j I)^{d_j} is invertible on that subspace. Indeed, on ker⁡(T−λjI)dj\ker(T-\lambda_j I)^{d_j}, the operator T−λjIT - \lambda_j I is nilpotent (its djd_j-th power is zero there by definition of the kernel), so T−λiI=(T−λjI)+(λj−λi)IT-\lambda_i I = (T - \lambda_j I) + (\lambda_j - \lambda_i) I is (nilpotent) ++ (nonzero scalar times identity, since λi≠λj\lambda_i \ne \lambda_j), which is invertible: if NN is nilpotent with Nr=0N^r=0 and c≠0c \ne 0, then N+cIN + cI is invertible with inverse 1c∑t=0r−1(−N/c)t\frac1c\sum_{t=0}^{r-1}(-N/c)^t, as one checks by direct multiplication (a telescoping geometric-series identity, valid since NN and II commute). Hence T−λiIT-\lambda_i I is invertible on ker⁡(T−λjI)dj\ker(T-\lambda_jI)^{d_j}, so any power (T−λiI)k(T-\lambda_iI)^k is also invertible there (composition of invertible maps). So from 0=∑j(T−λiI)kwj=(T−λiI)kwi+∑j≠i(T−λiI)kwj0 = \sum_j (T-\lambda_iI)^kw_j = (T-\lambda_i I)^k w_i + \sum_{j\ne i}(T-\lambda_i I)^k w_j, and the first term is already 00, we get ∑j≠i(T−λiI)kwj=0\sum_{j \ne i} (T-\lambda_i I)^k w_j = 0. Since (T−λiI)k(T - \lambda_iI)^k is injective on each ker⁡(T−λjI)dj\ker(T-\lambda_jI)^{d_j} for j≠ij \ne i (invertibility just shown), and these subspaces intersect trivially by directness of the sum, injectivity on each summand plus the vanishing of the total sum forces each term (T−λiI)kwj(T-\lambda_i I)^k w_j, j≠ij\ne i, to vanish individually: more precisely, apply the same argument used for uniqueness of direct-sum decompositions — the vectors (T−λiI)kwj(T-\lambda_iI)^kw_j for j≠ij \neq i lie in the respective ker⁡(T−λjI)dj\ker(T-\lambda_j I)^{d_j} (since these are TT-invariant, being kernels of a polynomial in TT, hence invariant under the commuting operator (T−λiI)k(T-\lambda_iI)^k), so their sum being 00 with the subspaces in direct sum forces each to be 00. By injectivity of (T−λiI)k(T-\lambda_iI)^k on ker⁡(T−λjI)dj\ker(T-\lambda_jI)^{d_j}, this gives wj=0w_j = 0 for all j≠ij \ne i. Therefore v=wi∈ker⁡(T−λiI)div = w_i \in \ker(T-\lambda_iI)^{d_i}, as required. This proves G(λi,T)=ker⁡(T−λiI)diG(\lambda_i, T) = \ker(T-\lambda_iI)^{d_i}, establishing part 3's claim that the generalized eigenspace equals this specific kernel, and combined with Step 0's decomposition proves part 2.

Step 2: Dimension count. From V=⨁iker⁡(T−λiI)di=⨁iG(λi,T)V = \bigoplus_i \ker(T-\lambda_iI)^{d_i} = \bigoplus_i G(\lambda_i,T) we get

n=dim⁡V=∑i=1mdim⁡G(λi,T).n = \dim V = \sum_{i=1}^m \dim G(\lambda_i,T).

We must show dim⁡G(λi,T)=di\dim G(\lambda_i,T) = d_i exactly (not merely that the dimensions sum correctly). Fix ii and let Ui=G(λi,T)=ker⁡(T−λiI)diU_i = G(\lambda_i,T) = \ker(T-\lambda_iI)^{d_i}. Since UiU_i is TT-invariant (kernel of a polynomial in TT commutes with TT: if (T−λiI)diu=0(T-\lambda_iI)^{d_i}u=0 then (T−λiI)di(Tu)=T(T−λiI)diu=0(T-\lambda_i I)^{d_i}(Tu) = T(T-\lambda_iI)^{d_i}u = 0), we may consider S:=T∣UiS := T|_{U_i}. On UiU_i, S−λiIS - \lambda_i I is nilpotent by construction (its did_i-th power is zero). Choose a basis of VV adapted to the direct sum V=U1⊕⋯⊕UmV = U_1\oplus\cdots\oplus U_m by concatenating bases of each UjU_j; in this basis TT is block diagonal with blocks T∣UjT|_{U_j}. The characteristic polynomial multiplies over a block-diagonal matrix:

χT(z)=∏j=1mχT∣Uj(z).\chi_T(z) = \prod_{j=1}^m \chi_{T|_{U_j}}(z).

Since T∣Ui−λiIT|_{U_i} - \lambda_i I is nilpotent on UiU_i (dimension dim⁡Ui\dim U_i), its only eigenvalue is 00 (a nilpotent operator's characteristic polynomial is zdim⁡Uiz^{\dim U_i}, since all eigenvalues of a nilpotent operator are 00 — if Nv=μvNv=\mu v with v≠0v\ne 0 then Nrv=μrvN^r v = \mu^r v, and Nr=0N^r=0 forces μr=0\mu^r=0 hence μ=0\mu=0, and since we're over C\mathbb C the char. poly of NN splits and can only have root 00). Hence χT∣Ui−λiI(z)=zdim⁡Ui\chi_{T|_{U_i}-\lambda_iI}(z) = z^{\dim U_i}, and shifting back,

χT∣Ui(z)=(z−λi)dim⁡Ui.\chi_{T|_{U_i}}(z) = (z - \lambda_i)^{\dim U_i}.

Substituting into the product formula:

χT(z)=∏j=1m(z−λj)dim⁡Uj.\chi_T(z) = \prod_{j=1}^m (z-\lambda_j)^{\dim U_j}.

But we already know from Step 0 that χT(z)=∏j(z−λj)dj\chi_T(z) = \prod_j (z-\lambda_j)^{d_j}, with the λj\lambda_j distinct. Unique factorization of polynomials over C\mathbb{C} (or over any field) into irreducible (here linear) factors forces the exponents to match termwise:

dim⁡Ui=difor every i.\dim U_i = d_i \quad \text{for every } i.

This proves part 3.

Step 3: Invariance and structure of the restriction. We already noted G(λi,T)=UiG(\lambda_i,T)=U_i is TT-invariant, proving part 1. Define Ni:=(T−λiI)∣UiN_i := (T-\lambda_iI)|_{U_i}, an operator on UiU_i. By construction Nidi=0N_i^{d_i} = 0 on UiU_i (since Ui=ker⁡(T−λiI)diU_i = \ker(T-\lambda_iI)^{d_i}), so NiN_i is nilpotent. And T∣Ui=λiI∣Ui+NiT|_{U_i} = \lambda_i I|_{U_i} + N_i by rearranging the definition of NiN_i. This proves part 4, and completes the proof of the theorem. ■\blacksquare

∎

Worked examples

Example 1 (a 4×44\times 4 operator with a repeated eigenvalue and a genuinely mixed structure). Let T:C4→C4T: \mathbb{C}^4 \to \mathbb{C}^4 be given, in the standard basis, by the matrix

A=(3100031000300005).A = \begin{pmatrix} 3 & 1 & 0 & 0\\ 0 & 3 & 1 & 0\\ 0 & 0 & 3 & 0\\ 0 & 0 & 0 & 5 \end{pmatrix}.

Characteristic polynomial. Since AA is upper triangular, χT(z)=(z−3)3(z−5)\chi_T(z) = (z-3)^3(z-5). So the distinct eigenvalues are λ1=3\lambda_1 = 3 with multiplicity d1=3d_1 = 3, and λ2=5\lambda_2 = 5 with multiplicity d2=1d_2 = 1. Note d1+d2=4=dim⁡Vd_1+d_2 = 4 = \dim V, consistent with the theorem. Computing G(3,T)=ker⁡(T−3I)3G(3,T) = \ker(T-3I)^3. We have

A−3I=(0100001000000002).A - 3I = \begin{pmatrix} 0&1&0&0\\0&0&1&0\\0&0&0&0\\0&0&0&2 \end{pmatrix}.

Compute (A−3I)2(A-3I)^2:

(A−3I)2=(0010000000000004),(A−3I)3=(0000000000000008).(A-3I)^2 = \begin{pmatrix} 0&0&1&0\\0&0&0&0\\0&0&0&0\\0&0&0&4 \end{pmatrix}, \qquad (A-3I)^3 = \begin{pmatrix} 0&0&0&0\\0&0&0&0\\0&0&0&0\\0&0&0&8 \end{pmatrix}.

Solving (A−3I)3v=0(A-3I)^3 v = 0 for v=(v1,v2,v3,v4)Tv = (v_1,v_2,v_3,v_4)^T: the only nonzero row is the last, giving 8v4=08v_4 = 0, so v4=0v_4 = 0 and v1,v2,v3v_1,v_2,v_3 free. Thus

G(3,T)=ker⁡(A−3I)3=span{e1,e2,e3},G(3,T) = \ker(A-3I)^3 = \mathrm{span}\{e_1,e_2,e_3\},

a 3-dimensional space, matching d1=3d_1 = 3 exactly as the theorem predicts — and one checks that ker⁡(A−3I)2\ker(A-3I)^2 already equals this same 3-dimensional span too (the third power was not even needed here, since (A−3I)2(A-3I)^2 restricted to the first three coordinates is already the whole nilpotent block's top power), while ker⁡(A−3I)\ker(A-3I) is only 1-dimensional (span of e1e_1), confirming that ordinary eigenvectors for λ=3\lambda=3 form a proper subspace of G(3,T)G(3,T): diagonalization fails on this block, but the generalized eigenspace repairs it. Computing G(5,T)G(5,T). Here A−5IA - 5I has (4,4)(4,4) entry 00 and the top-left 3×33\times3 block is (−2100−2100−2)\begin{pmatrix}-2&1&0\\0&-2&1\\0&0&-2\end{pmatrix}, invertible (determinant −8≠0-8 \ne 0). So ker⁡(A−5I)=span{e4}\ker(A-5I) = \mathrm{span}\{e_4\}, already 11-dimensional =d2= d_2, so G(5,T)=ker⁡(A−5I)1=span{e4}G(5,T) = \ker(A-5I)^1 = \mathrm{span}\{e_4\} and no higher power is needed. Verifying the decomposition. Indeed C4=span{e1,e2,e3}⊕span{e4}=G(3,T)⊕G(5,T)\mathbb{C}^4 = \mathrm{span}\{e_1,e_2,e_3\} \oplus \mathrm{span}\{e_4\} = G(3,T)\oplus G(5,T), dimensions 3+1=43+1=4 check out, and both pieces are visibly TT-invariant since AA is block upper-triangular with these exact blocks.

Example 2 (a genuinely non-triangular matrix, forcing real computation of the kernels). Let T:C3→C3T:\mathbb C^3\to\mathbb C^3 be given by

B=(41−1−121211).B = \begin{pmatrix} 4 & 1 & -1\\ -1 & 2 & 1\\ 2 & 1 & 1 \end{pmatrix}.

Characteristic polynomial. Compute χT(z)=det⁡(zI−B)\chi_T(z) = \det(zI - B). Trace =4+2+1=7= 4+2+1 = 7. Sum of principal 2×22\times2 minors: for pairs (1,2)(1,2): ∣41−12∣=8+1=9\begin{vmatrix}4&1\\-1&2\end{vmatrix}=8+1=9; for (1,3)(1,3): ∣4−121∣=4+2=6\begin{vmatrix}4&-1\\2&1\end{vmatrix}=4+2=6; for (2,3)(2,3): ∣2111∣=2−1=1\begin{vmatrix}2&1\\1&1\end{vmatrix}=2-1=1. Sum =9+6+1=16=9+6+1=16. Determinant of BB: expand along first row,

det⁡B=4(2⋅1−1⋅1)−1((−1)⋅1−1⋅2)+(−1)((−1)⋅1−2⋅2)=4(1)−1(−3)−1(−5)=4+3+5=12.\det B = 4(2\cdot1 - 1\cdot1) - 1((-1)\cdot1-1\cdot2) + (-1)((-1)\cdot1-2\cdot2) = 4(1) -1(-3) -1(-5) = 4+3+5=12.

So χT(z)=z3−7z2+16z−12\chi_T(z) = z^3 - 7z^2 + 16z - 12. Finding roots. Try z=2z=2: 8−28+32−12=08 - 28+32-12=0. So z=2z=2 is a root. Divide: z3−7z2+16z−12=(z−2)(z2−5z+6)=(z−2)(z−2)(z−3)z^3-7z^2+16z-12 = (z-2)(z^2-5z+6) = (z-2)(z-2)(z-3). So

χT(z)=(z−2)2(z−3).\chi_T(z) = (z-2)^2(z-3).

Eigenvalues: λ1=2\lambda_1 = 2 with d1=2d_1=2, λ2=3\lambda_2=3 with d2=1d_2=1. Ordinary eigenspace at λ=2\lambda=2. Solve (B−2I)v=0(B-2I)v=0:

B−2I=(21−1−10121−1).B - 2I = \begin{pmatrix}2&1&-1\\-1&0&1\\2&1&-1\end{pmatrix}.

Rows 1 and 3 are identical, so rank ≤2\le 2; row-reduce: R1:2v1+v2−v3=0R_1: 2v_1+v_2-v_3=0, R2:−v1+v3=0⇒v3=v1R_2: -v_1+v_3=0 \Rightarrow v_3=v_1. Substitute into R1R_1: 2v1+v2−v1=0⇒v2=−v12v_1+v_2-v_1=0\Rightarrow v_2=-v_1. So the ordinary eigenspace is span{(1,−1,1)}\mathrm{span}\{(1,-1,1)\}, only 1-dimensional — strictly smaller than d1=2d_1=2. This already shows TT is not diagonalizable, and we must pass to (B−2I)2(B-2I)^2. Generalized eigenspace at λ=2\lambda=2. Compute (B−2I)2(B-2I)^2:

(B−2I)2=(21−1−10121−1)(21−1−10121−1).(B-2I)^2 = \begin{pmatrix}2&1&-1\\-1&0&1\\2&1&-1\end{pmatrix}\begin{pmatrix}2&1&-1\\-1&0&1\\2&1&-1\end{pmatrix}.

Row 1 of product: (2⋅2+1⋅(−1)+(−1)⋅2, 2⋅1+1⋅0+(−1)⋅1, 2⋅(−1)+1⋅1+(−1)⋅(−1))=(4−1−2, 2+0−1, −2+1+1)=(1,1,0)(2\cdot2+1\cdot(-1)+(-1)\cdot2,\ 2\cdot1+1\cdot0+(-1)\cdot1,\ 2\cdot(-1)+1\cdot1+(-1)\cdot(-1)) = (4-1-2,\ 2+0-1,\ -2+1+1) = (1,1,0). Row 2: (−1⋅2+0⋅(−1)+1⋅2, −1⋅1+0⋅0+1⋅1, −1⋅(−1)+0⋅1+1⋅(−1))=(−2+0+2, −1+0+1, 1+0−1)=(0,0,0)(-1\cdot2+0\cdot(-1)+1\cdot2,\ -1\cdot1+0\cdot0+1\cdot1,\ -1\cdot(-1)+0\cdot1+1\cdot(-1)) = (-2+0+2,\ -1+0+1,\ 1+0-1) = (0,0,0). Row 3 equals Row 1 since original rows 1,3 agreed: (1,1,0)(1,1,0). So

(B−2I)2=(110000110).(B-2I)^2 = \begin{pmatrix}1&1&0\\0&0&0\\1&1&0\end{pmatrix}.

Solve (B−2I)2v=0(B-2I)^2 v = 0: only independent equation is v1+v2=0v_1+v_2=0, i.e. v2=−v1v_2=-v_1, with v3v_3 free. This is a 2-dimensional solution space:

G(2,T)=ker⁡(B−2I)2=span{(1,−1,0), (0,0,1)},G(2,T) = \ker(B-2I)^2 = \mathrm{span}\{(1,-1,0),\,(0,0,1)\},

matching d1=2d_1 = 2 exactly, as guaranteed by the theorem. Eigenspace at λ=3\lambda=3. Solve (B−3I)v=0(B-3I)v=0 with B−3I=(11−1−1−1121−2)B-3I = \begin{pmatrix}1&1&-1\\-1&-1&1\\2&1&-2\end{pmatrix}. Rows 1,2 are negatives of each other; use row 1: v1+v2−v3=0v_1+v_2-v_3=0. Row 3: 2v1+v2−2v3=02v_1+v_2-2v_3=0. Subtract row1 from row3: v1−v3=0⇒v1=v3v_1 - v_3 = 0 \Rightarrow v_1=v_3; then row1 gives v2=v3−v1=0v_2 = v_3-v_1=0. So eigenspace is span{(1,0,1)}\mathrm{span}\{(1,0,1)\}, dimension 1=d21=d_2, so G(3,T)=ker⁡(B−3I)G(3,T) = \ker(B-3I), no higher power needed. Verification of the decomposition. Basis {(1,−1,0),(0,0,1),(1,0,1)}\{(1,-1,0),(0,0,1),(1,0,1)\} has determinant

det⁡(101−100011)\det\begin{pmatrix}1&0&1\\-1&0&0\\0&1&1\end{pmatrix}

(columns as the three vectors) =1(0⋅1−0⋅1)−0+1((−1)⋅1−0⋅0)=0+0−1=−1≠0= 1(0\cdot1 - 0\cdot1) - 0 + 1((-1)\cdot1 - 0\cdot0) = 0 + 0 -1 = -1 \ne 0, so these three vectors are linearly independent and hence form a basis of C3\mathbb{C}^3, confirming

C3=G(2,T)⊕G(3,T),\mathbb{C}^3 = G(2,T) \oplus G(3,T),

with dimensions 2+1=32 + 1 = 3 as required.

Counterexamplewhy algebraic closedness of the field cannot be dropped

Consider V=R2V = \mathbb{R}^2 and T:R2→R2T: \mathbb R^2 \to \mathbb R^2 given by the rotation-type matrix

A=(0−110),χT(z)=z2+1.A = \begin{pmatrix} 0 & -1\\ 1 & 0 \end{pmatrix}, \qquad \chi_T(z) = z^2+1.

This polynomial has no real roots (z2=−1z^2 = -1 has no solution in R\mathbb{R}), so TT has no eigenvalues at all over R\mathbb{R}: there is no λ∈R\lambda \in \mathbb R with det⁡(λI−A)=0\det(\lambda I - A) = 0. Consequently there is not even a single nonzero generalized eigenspace G(λ,T)G(\lambda,T) to speak of (each would require λ\lambda to be a real eigenvalue, and none exists), so the purported decomposition

V=⨁λ∈RG(λ,T)V = \bigoplus_{\lambda \in \mathbb R} G(\lambda,T)

is the empty direct sum {0}\{0\}, which is certainly not equal to V=R2V=\mathbb R^2. The theorem's proof breaks down exactly at Step 0: over R\mathbb R the characteristic polynomial z2+1z^2+1 does not factor into linear factors, so Cayley–Hamilton only gives T2+I=0T^2 + I = 0, an irreducible quadratic relation, and Lemma A (which needs the modulus polynomials pi(z)=(z−λi)dip_i(z)=(z-\lambda_i)^{d_i} to be linear-power factors summing in degree to dim⁡V\dim V) has no valid application — there are no real linear factors to extract. The fix, working over C\mathbb C, is immediate: viewing the same matrix as an operator on C2\mathbb{C}^2, χT(z)=z2+1=(z−i)(z+i)\chi_T(z)=z^2+1=(z-i)(z+i) factors completely, λ1=i,λ2=−i\lambda_1=i,\lambda_2=-i are honest eigenvalues, and C2=G(i,T)⊕G(−i,T)\mathbb C^2 = G(i,T)\oplus G(-i,T) holds exactly as the theorem asserts. This shows algebraic closedness of the scalar field is not a cosmetic hypothesis but essential to the existence of any eigenvalues to decompose along.

Scope, edge cases & common traps

SituationWhat happens
TT diagonalizableEach G(λi,T)G(\lambda_i,T) equals the ordinary eigenspace ker⁡(T−λiI)\ker(T-\lambda_iI); nilpotent part Ni=0N_i = 0
TT has a repeated eigenvalue but is still diagonalizabledim⁡G(λi,T)=di\dim G(\lambda_i,T)=d_i still holds, and equals the geometric multiplicity too, since Ni=0N_i=0
Field not algebraically closed (e.g. R\mathbb R)Characteristic polynomial may not split; theorem as stated fails (see counterexample)
Infinite-dimensional VVStabilization of the kernel chain ker⁡(T−λI)k\ker(T-\lambda I)^k at finite kk is not automatic; theorem requires finite dimension
Computing G(λ,T)G(\lambda,T) in practiceAlways safe to use k=n=dim⁡Vk=n=\dim V; often stabilizes much earlier, as in Example 1

The mathematician behind it

Origins · 1 minMarie Ennemond Camille Jordan and how this was discovered
ME
Marie Ennemond Camille Jordan
1838–1922 · 19th-century French analysis and algebra

Camille Jordan worked in Paris across analysis, group theory, and linear algebra, and is remembered above all for the canonical form that bears his name — the ultimate refinement of the generalized eigenspace decomposition into an explicit basis of chains. His 1870 treatise Traité des substitutions et des équations algébriques built the theory of permutation groups and matrix canonical forms together, reflecting his conviction that structural classification (of groups, of curves, of operators) was the right unifying language for algebra and analysis. The decomposition described in this article is the essential intermediate step his form requires: first split the space by eigenvalue into generalized eigenspaces, then classify the nilpotent piece within each block via chains, producing the full Jordan structure.

History
- 1870 — Jordan publishes the Traité des substitutions, introducing what becomes the Jordan canonical form for matrices over C\mathbb{C}.
- 1878 — Jordan's canonical form work is further clarified and connected to invariant factor theory by Frobenius and others working over general fields.
- 1892 — Weierstrass's elementary divisor theory, developed independently, is recognized as an equivalent formulation of the same structural decomposition.
- 1930s — Modern linear algebra textbooks formalize generalized eigenspaces and the primary decomposition theorem using module-theoretic language over C[z]\mathbb C[z].
- 1970s–present — the decomposition becomes a standard capstone result in undergraduate linear algebra courses, preceding the Jordan form.
Is G(λ,T)G(\lambda,T) always equal to ker⁡(T−λI)dim⁡V\ker(T-\lambda I)^{\dim V}, or can a smaller power suffice?

The union ⋃kker⁡(T−λI)k\bigcup_k \ker(T-\lambda I)^k always stabilizes by k=dim⁡Vk=\dim V at the very latest, since the increasing chain of subspaces cannot keep strictly growing past dimension dim⁡V\dim V many steps. However, in practice it very often stabilizes earlier — specifically by k=dk=d, the algebraic multiplicity of λ\lambda, which is generally much smaller than dim⁡V\dim V when there are several distinct eigenvalues. Both examples above stabilized before reaching dim⁡V\dim V. Using k=dim⁡Vk = \dim V is always a safe, if sometimes wasteful, upper bound for hand computation.

What exactly goes wrong if the field is not algebraically closed?

The proof relies on Cayley–Hamilton producing a complete factorization of χT\chi_T into linear factors (z−λi)di(z-\lambda_i)^{d_i}; only then can Lemma A be applied to split VV along these factors. Over a non-algebraically-closed field like R\mathbb R, χT\chi_T may have irreducible quadratic (or higher-degree) factors with no real roots, so there may be too few (or zero) real eigenvalues to exhaust VV. The rotation matrix counterexample shows this explicitly: R2\mathbb R^2 cannot be written as a sum of real generalized eigenspaces at all, since no real eigenvalues exist, while the same matrix over C\mathbb C decomposes perfectly.

How does this theorem relate to the Jordan Canonical Form?

This theorem is the first of two stages in producing the Jordan Canonical Form. Stage one (this article) splits VV into generalized eigenspaces G(λi,T)G(\lambda_i,T), on each of which TT acts as λiI\lambda_i I plus a nilpotent operator NiN_i. Stage two applies the classification of nilpotent operators (every nilpotent operator has a basis of Jordan chains) to each NiN_i separately, producing an explicit basis in which the whole matrix of TT is block diagonal with Jordan blocks. Without the decomposition theorem, there would be no valid way to isolate each eigenvalue's "own" nilpotent structure in the first place.

Does the theorem say anything about uniqueness of the decomposition?

Yes: the decomposition V=⨁iG(λi,T)V = \bigoplus_i G(\lambda_i,T) is completely canonical, with no choices involved, because each G(λi,T)G(\lambda_i,T) is intrinsically defined as ker⁡(T−λiI)di\ker(T-\lambda_iI)^{d_i}, depending only on TT and λi\lambda_i, not on any basis. This is stronger than the eventual Jordan form itself, whose basis (the specific Jordan chains chosen) is not unique, although the resulting block sizes are. The generalized eigenspaces themselves, as subspaces, are uniquely determined by TT.

Can two different eigenvalues share vectors in their generalized eigenspaces?

No. Part 2 of the theorem asserts the sum is direct, meaning G(λi,T)∩G(λj,T)={0}G(\lambda_i,T)\cap G(\lambda_j,T)=\{0\} for i≠ji\ne j. This was proved via the invertibility argument in the proof: on G(λj,T)G(\lambda_j,T), the operator T−λiIT-\lambda_iI is invertible whenever λi≠λj\lambda_i \ne \lambda_j (nilpotent plus nonzero scalar), so no nonzero vector of G(λj,T)G(\lambda_j,T) can also lie in ker⁡(T−λiI)k\ker(T-\lambda_iI)^k for any kk, which is precisely what membership in G(λi,T)G(\lambda_i,T) would require.

What is the geometric meaning of dim⁡G(λ,T)\dim G(\lambda,T) versus the geometric multiplicity dim⁡ker⁡(T−λI)\dim\ker(T-\lambda I)?

The algebraic multiplicity d=dim⁡G(λ,T)d = \dim G(\lambda,T) counts how many times (z−λ)(z-\lambda) divides the characteristic polynomial, while the geometric multiplicity dim⁡ker⁡(T−λI)\dim\ker(T-\lambda I) counts actual linearly independent eigenvectors. Always dim⁡ker⁡(T−λI)≤dim⁡G(λ,T)=d\dim\ker(T-\lambda I) \le \dim G(\lambda,T) = d, with equality exactly when TT restricted to G(λ,T)G(\lambda,T) has zero nilpotent part, i.e. when TT is diagonalizable on that block. The gap between the two multiplicities measures precisely how far TT is from being diagonalizable at λ\lambda.

Before this

Where this leads