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 for some k≥1, not necessarily k=1.
The generalized eigenspace G(λ,T) is the union (equivalently, for large enough k, the single subspace) of these kernels.
Over C, every operator's generalized eigenspaces span the whole space and intersect trivially: V=G(λ1,T)⊕⋯⊕G(λm,T).
The dimension of each generalized eigenspace equals the algebraic multiplicity of λ in the characteristic polynomial.
Each G(λ,T) is T-invariant, and T restricted there equals λI 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 etA in systems of linear ODEs: writing A=∑i(λiPi+Ni) on each block lets one compute etA explicitly as ∑ieλit(I+tNi+⋯+tdi−1Nidi−1/(di−1)!)Pi, 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 V be a finite-dimensional vector space over a field F, and let T:V→V be a linear operator. Let λ∈F. A nonzero vector v∈V is a generalized eigenvector of T corresponding to λ if
(T−λI)kv=0
for some positive integer k. (If k=1 works, v is an ordinary eigenvector.) The generalized eigenspace of T corresponding to λ is
G(λ,T)={v∈V:(T−λI)kv=0 for some k≥1}=k=1⋃∞ker(T−λI)k.
Since ker(T−λI)k⊆ker(T−λI)k+1 for every k (if (T−λI)kv=0 then (T−λI)k+1v=0), and dimV<∞, this increasing chain of subspaces stabilizes. In fact it stabilizes by k=n:=dimV, so
G(λ,T)=ker(T−λI)n.
In particular G(λ,T) is a subspace of V, and it is nonzero precisely when λ is an eigenvalue of T.
Intuition
Think of an eigenvalue λ as a "resonant frequency" of T: ordinary eigenvectors are directions along which T acts by pure scaling. When T is not diagonalizable, some directions are almost eigenvectors — T moves them to λ times themselves plus a small correction, and that correction itself gets killed by a further application of T−λI. A generalized eigenvector of "rank k" is a vector requiring exactly k applications of T−λI to reach zero; feeding it through T−λI repeatedly produces a chain
vk↦vk−1↦⋯↦v1↦0,
where each vi is a genuine eigenvector once i=1. This chain is precisely the seed of a Jordan block. The generalized eigenspace G(λ,T) collects all vectors belonging to some such chain for the given λ; 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
TheoremGeneralized Eigenspace Decomposition
Let V be a finite-dimensional vector space over C with dimV=n≥1, and let T:V→V be a linear operator. Let λ1,…,λm be the distinct eigenvalues of T. Then: 1. (Invariance) Each G(λi,T) is a T-invariant subspace of V. 2. (Direct sum decomposition)
V=G(λ1,T)⊕G(λ2,T)⊕⋯⊕G(λm,T).
3. (Dimension formula) If di is the multiplicity of λi as a root of the characteristic polynomial χT(z)=det(zI−T), then
dimG(λi,T)=difor each i,
and consequently G(λi,T)=ker(T−λiI)di. 4. (Structure of the pieces) For each i, the restriction T∣G(λi,T) equals λiI+Ni where Ni is a nilpotent operator on G(λi,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) satisfies χT(T)=0. Since C is algebraically closed, χT factors completely:
χT(z)=i=1∏m(z−λi)di,i=1∑mdi=n,
where λ1,…,λm are the distinct roots (eigenvalues) and di≥1 their multiplicities. Since χT(T)=0, we have
i=1∏m(T−λiI)di=0as an operator on V.(∗)
Lemma A (kernels of coprime polynomials in T split the space).Suppose p1(z),…,pm(z) are polynomials, pairwise coprime (no common root, since we work over C this means no shared linear factor), such that p1(T)p2(T)⋯pm(T)=0. Let Wi=kerpi(T). Then V=W1⊕⋯⊕Wm. *Proof of Lemma A. Apply this with pi(z)=(z−λi)di; these are pairwise coprime since the λi are distinct. We prove the general lemma by induction on m. Base case m=1: trivial, p1(T)=0 forces W1=kerp1(T)=V. Case m=2:* Since p1,p2 are coprime, by Bézout's identity for polynomials over the field C, there exist polynomials a(z),b(z) with
a(z)p1(z)+b(z)p2(z)=1.
Substituting T (all these are polynomials in the single operator T, so they commute):
a(T)p1(T)+b(T)p2(T)=I.
For v∈V, write
v=a(T)p1(T)v+b(T)p2(T)v=:v2+v1.
Since p1(T)p2(T)=0 (as p1p2 divides the given product, or directly from (∗) when m=2), we get p2(T)v2=p2(T)a(T)p1(T)v=a(T)(p1(T)p2(T))v=0, so v2∈kerp2(T)=W2. Similarly v1=b(T)p2(T)v∈kerp1(T)=W1 because p1(T)v1=b(T)p1(T)p2(T)v=0. This shows V=W1+W2. For the sum to be direct, suppose w1+w2=0 with wi∈Wi. Applying a(T)p1(T) to 0=w1+w2: a(T)p1(T)w1+a(T)p1(T)w2=0. But a(T)p1(T)w2=a(T)p1(T)w2; using a(T)p1(T)=I−b(T)p2(T), we get a(T)p1(T)w2=w2−b(T)p2(T)w2=w2−0=w2 since p2(T)w2=0. Meanwhile a(T)p1(T)w1=0 because p1(T)w1=0. Hence the equation becomes 0+w2=0, i.e. w2=0, and then w1=0 too. So W1∩W2={0}, and V=W1⊕W2. Inductive step: Suppose the lemma holds for m−1 pairwise coprime polynomials. Given p1,…,pm pairwise coprime with ∏pi(T)=0, set q(z)=∏i=2mpi(z). Since p1 is coprime to each pi (i≥2), and coprimality of p1 with each factor of q implies coprimality of p1 with q (a standard property of coprime polynomials: if gcd(p1,pi)=1 for all i, then gcd(p1,∏pi)=1, provable by Bézout multiplicatively), p1 and q are coprime. Also p1(T)q(T)=∏ipi(T)=0. By the m=2 case, V=kerp1(T)⊕kerq(T)=W1⊕kerq(T). Now restrict attention to U:=kerq(T). We claim U=kerp2(T)⊕⋯⊕kerpm(T) inside U, using the inductive hypothesis applied to T∣U (note U is T-invariant since T commutes with q(T), so q(T)Tu=Tq(T)u=0 for u∈U). We have q(T∣U)=0 by definition of U, matching the hypothesis of the inductive claim for p2,…,pm pairwise coprime. Hence U=ker(p2(T)∣U)⊕⋯⊕ker(pm(T)∣U). Since for i≥2, ker(pi(T)∣U)=kerpi(T)∩U=kerpi(T) — the last equality because kerpi(T)⊆U=kerq(T) automatically (if pi(T)v=0 then q(T)v=0 too, since pi∣q) — we conclude
U=kerp2(T)⊕⋯⊕kerpm(T)=W2⊕⋯⊕Wm.
Combining, V=W1⊕U=W1⊕W2⊕⋯⊕Wm. This completes the induction and proves Lemma A. ■
Applying Lemma A. Take pi(z)=(z−λi)di in (∗). These are pairwise coprime (distinct linear factors, over C coprimality of powers of distinct linear polynomials is immediate: any common root would have to be a root of both z−λi and z−λj, impossible for λi=λj). Lemma A gives
V=ker(T−λ1I)d1⊕⋯⊕ker(T−λmI)dm.
Step 1: Identify ker(T−λiI)di with G(λi,T). By definition G(λi,T)=⋃kker(T−λiI)k, and this chain stabilizes at k=n=dimV at the latest (shown in the definition above). We must show it in fact stabilizes by k=di, matching the exponent from Cayley–Hamilton, and that no more room is needed. First, ker(T−λiI)di⊆G(λi,T) trivially, by definition of G(λi,T) as a union including the k=di term. Conversely, we show G(λi,T)⊆ker(T−λiI)di, i.e. the chain of kernels for λi has already stabilized by step di. Suppose v∈ker(T−λiI)k for some k>di; we show v∈ker(T−λiI)di. Write v=∑jwj with wj∈G(λj,T)=ker(T−λjI)dj 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:
0=(T−λiI)kv=j∑(T−λiI)kwj.
For j=i: since wi∈ker(T−λiI)di and k>di≥di, certainly (T−λiI)kwi=0 as well (apply extra copies of T−λiI to the already-zero vector (T−λiI)diwi). For j=i: we claim (T−λiI) restricted to ker(T−λjI)dj is invertible on that subspace. Indeed, on ker(T−λjI)dj, the operator T−λjI is nilpotent (its dj-th power is zero there by definition of the kernel), so T−λiI=(T−λjI)+(λj−λi)I is (nilpotent) + (nonzero scalar times identity, since λi=λj), which is invertible: if N is nilpotent with Nr=0 and c=0, then N+cI is invertible with inverse c1∑t=0r−1(−N/c)t, as one checks by direct multiplication (a telescoping geometric-series identity, valid since N and I commute). Hence T−λiI is invertible on ker(T−λjI)dj, so any power (T−λiI)k is also invertible there (composition of invertible maps). So from 0=∑j(T−λiI)kwj=(T−λiI)kwi+∑j=i(T−λiI)kwj, and the first term is already 0, we get ∑j=i(T−λiI)kwj=0. Since (T−λiI)k is injective on each ker(T−λjI)dj for j=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, j=i, to vanish individually: more precisely, apply the same argument used for uniqueness of direct-sum decompositions — the vectors (T−λiI)kwj for j=i lie in the respective ker(T−λjI)dj (since these are T-invariant, being kernels of a polynomial in T, hence invariant under the commuting operator (T−λiI)k), so their sum being 0 with the subspaces in direct sum forces each to be 0. By injectivity of (T−λiI)k on ker(T−λjI)dj, this gives wj=0 for all j=i. Therefore v=wi∈ker(T−λiI)di, as required. This proves G(λi,T)=ker(T−λiI)di, 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) we get
n=dimV=i=1∑mdimG(λi,T).
We must show dimG(λi,T)=di exactly (not merely that the dimensions sum correctly). Fix i and let Ui=G(λi,T)=ker(T−λiI)di. Since Ui is T-invariant (kernel of a polynomial in T commutes with T: if (T−λiI)diu=0 then (T−λiI)di(Tu)=T(T−λiI)diu=0), we may consider S:=T∣Ui. On Ui, S−λiI is nilpotent by construction (its di-th power is zero). Choose a basis of V adapted to the direct sum V=U1⊕⋯⊕Um by concatenating bases of each Uj; in this basis T is block diagonal with blocks T∣Uj. The characteristic polynomial multiplies over a block-diagonal matrix:
χT(z)=j=1∏mχT∣Uj(z).
Since T∣Ui−λiI is nilpotent on Ui (dimension dimUi), its only eigenvalue is 0 (a nilpotent operator's characteristic polynomial is zdimUi, since all eigenvalues of a nilpotent operator are 0 — if Nv=μv with v=0 then Nrv=μrv, and Nr=0 forces μr=0 hence μ=0, and since we're over C the char. poly of N splits and can only have root 0). Hence χT∣Ui−λiI(z)=zdimUi, and shifting back,
χT∣Ui(z)=(z−λi)dimUi.
Substituting into the product formula:
χT(z)=j=1∏m(z−λj)dimUj.
But we already know from Step 0 that χT(z)=∏j(z−λj)dj, with the λj distinct. Unique factorization of polynomials over C (or over any field) into irreducible (here linear) factors forces the exponents to match termwise:
dimUi=difor every i.
This proves part 3.
Step 3: Invariance and structure of the restriction. We already noted G(λi,T)=Ui is T-invariant, proving part 1. Define Ni:=(T−λiI)∣Ui, an operator on Ui. By construction Nidi=0 on Ui (since Ui=ker(T−λiI)di), so Ni is nilpotent. And T∣Ui=λiI∣Ui+Ni by rearranging the definition of Ni. This proves part 4, and completes the proof of the theorem. ■
∎
Worked examples
Example 1 (a 4×4 operator with a repeated eigenvalue and a genuinely mixed structure). Let T:C4→C4 be given, in the standard basis, by the matrix
A=3000130001300005.
Characteristic polynomial. Since A is upper triangular, χT(z)=(z−3)3(z−5). So the distinct eigenvalues are λ1=3 with multiplicity d1=3, and λ2=5 with multiplicity d2=1. Note d1+d2=4=dimV, consistent with the theorem. Computing G(3,T)=ker(T−3I)3. We have
Solving (A−3I)3v=0 for v=(v1,v2,v3,v4)T: the only nonzero row is the last, giving 8v4=0, so v4=0 and v1,v2,v3 free. Thus
G(3,T)=ker(A−3I)3=span{e1,e2,e3},
a 3-dimensional space, matching d1=3 exactly as the theorem predicts — and one checks that ker(A−3I)2 already equals this same 3-dimensional span too (the third power was not even needed here, since (A−3I)2 restricted to the first three coordinates is already the whole nilpotent block's top power), while ker(A−3I) is only 1-dimensional (span of e1), confirming that ordinary eigenvectors for λ=3 form a proper subspace of G(3,T): diagonalization fails on this block, but the generalized eigenspace repairs it. Computing G(5,T). Here A−5I has (4,4) entry 0 and the top-left 3×3 block is −2001−2001−2, invertible (determinant −8=0). So ker(A−5I)=span{e4}, already 1-dimensional =d2, so G(5,T)=ker(A−5I)1=span{e4} and no higher power is needed. Verifying the decomposition. Indeed C4=span{e1,e2,e3}⊕span{e4}=G(3,T)⊕G(5,T), dimensions 3+1=4 check out, and both pieces are visibly T-invariant since A is block upper-triangular with these exact blocks.
Example 2 (a genuinely non-triangular matrix, forcing real computation of the kernels). Let T:C3→C3 be given by
B=4−12121−111.
Characteristic polynomial. Compute χT(z)=det(zI−B). Trace =4+2+1=7. Sum of principal 2×2 minors: for pairs (1,2): 4−112=8+1=9; for (1,3): 42−11=4+2=6; for (2,3): 2111=2−1=1. Sum =9+6+1=16. Determinant of B: expand along first row,
So χT(z)=z3−7z2+16z−12. Finding roots. Try z=2: 8−28+32−12=0. So z=2 is a root. Divide: z3−7z2+16z−12=(z−2)(z2−5z+6)=(z−2)(z−2)(z−3). So
χT(z)=(z−2)2(z−3).
Eigenvalues: λ1=2 with d1=2, λ2=3 with d2=1. Ordinary eigenspace at λ=2. Solve (B−2I)v=0:
B−2I=2−12101−11−1.
Rows 1 and 3 are identical, so rank ≤2; row-reduce: R1:2v1+v2−v3=0, R2:−v1+v3=0⇒v3=v1. Substitute into R1: 2v1+v2−v1=0⇒v2=−v1. So the ordinary eigenspace is span{(1,−1,1)}, only 1-dimensional — strictly smaller than d1=2. This already shows T is not diagonalizable, and we must pass to (B−2I)2. Generalized eigenspace at λ=2. Compute (B−2I)2:
(B−2I)2=2−12101−11−12−12101−11−1.
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). 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). Row 3 equals Row 1 since original rows 1,3 agreed: (1,1,0). So
(B−2I)2=101101000.
Solve (B−2I)2v=0: only independent equation is v1+v2=0, i.e. v2=−v1, with v3 free. This is a 2-dimensional solution space:
G(2,T)=ker(B−2I)2=span{(1,−1,0),(0,0,1)},
matching d1=2 exactly, as guaranteed by the theorem. Eigenspace at λ=3. Solve (B−3I)v=0 with B−3I=1−121−11−11−2. Rows 1,2 are negatives of each other; use row 1: v1+v2−v3=0. Row 3: 2v1+v2−2v3=0. Subtract row1 from row3: v1−v3=0⇒v1=v3; then row1 gives v2=v3−v1=0. So eigenspace is span{(1,0,1)}, dimension 1=d2, so G(3,T)=ker(B−3I), no higher power needed. Verification of the decomposition. Basis {(1,−1,0),(0,0,1),(1,0,1)} has determinant
det1−10001101
(columns as the three vectors) =1(0⋅1−0⋅1)−0+1((−1)⋅1−0⋅0)=0+0−1=−1=0, so these three vectors are linearly independent and hence form a basis of C3, confirming
C3=G(2,T)⊕G(3,T),
with dimensions 2+1=3 as required.
Counterexamplewhy algebraic closedness of the field cannot be dropped
Consider V=R2 and T:R2→R2 given by the rotation-type matrix
A=(01−10),χT(z)=z2+1.
This polynomial has no real roots (z2=−1 has no solution in R), so T has no eigenvalues at all over R: there is no λ∈R with det(λI−A)=0. Consequently there is not even a single nonzero generalized eigenspace G(λ,T) to speak of (each would require λ to be a real eigenvalue, and none exists), so the purported decomposition
V=λ∈R⨁G(λ,T)
is the empty direct sum {0}, which is certainly not equal to V=R2. The theorem's proof breaks down exactly at Step 0: over R the characteristic polynomial z2+1 does not factor into linear factors, so Cayley–Hamilton only gives T2+I=0, an irreducible quadratic relation, and Lemma A (which needs the modulus polynomials pi(z)=(z−λi)di to be linear-power factors summing in degree to dimV) has no valid application — there are no real linear factors to extract. The fix, working over C, is immediate: viewing the same matrix as an operator on C2, χT(z)=z2+1=(z−i)(z+i) factors completely, λ1=i,λ2=−i are honest eigenvalues, and C2=G(i,T)⊕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
Situation
What happens
T diagonalizable
Each G(λi,T) equals the ordinary eigenspace ker(T−λiI); nilpotent part Ni=0
T has a repeated eigenvalue but is still diagonalizable
dimG(λi,T)=di still holds, and equals the geometric multiplicity too, since Ni=0
Field not algebraically closed (e.g. R)
Characteristic polynomial may not split; theorem as stated fails (see counterexample)
Infinite-dimensional V
Stabilization of the kernel chain ker(T−λI)k at finite k is not automatic; theorem requires finite dimension
Computing G(λ,T) in practice
Always safe to use k=n=dimV; 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.
- 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].
- 1970s–present — the decomposition becomes a standard capstone result in undergraduate linear algebra courses, preceding the Jordan form.
Is G(λ,T) always equal to ker(T−λI)dimV, or can a smaller power suffice?
The union ⋃kker(T−λI)k always stabilizes by k=dimV at the very latest, since the increasing chain of subspaces cannot keep strictly growing past dimension dimV many steps. However, in practice it very often stabilizes earlier — specifically by k=d, the algebraic multiplicity of λ, which is generally much smaller than dimV when there are several distinct eigenvalues. Both examples above stabilized before reaching dimV. Using k=dimV 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 into linear factors (z−λi)di; only then can Lemma A be applied to split V along these factors. Over a non-algebraically-closed field like R, χT may have irreducible quadratic (or higher-degree) factors with no real roots, so there may be too few (or zero) real eigenvalues to exhaust V. The rotation matrix counterexample shows this explicitly: R2 cannot be written as a sum of real generalized eigenspaces at all, since no real eigenvalues exist, while the same matrix over 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 V into generalized eigenspaces G(λi,T), on each of which T acts as λiI plus a nilpotent operator Ni. Stage two applies the classification of nilpotent operators (every nilpotent operator has a basis of Jordan chains) to each Ni separately, producing an explicit basis in which the whole matrix of T 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) is completely canonical, with no choices involved, because each G(λi,T) is intrinsically defined as ker(T−λiI)di, depending only on T and λi, 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 T.
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} for i=j. This was proved via the invertibility argument in the proof: on G(λj,T), the operator T−λiI is invertible whenever λi=λj (nilpotent plus nonzero scalar), so no nonzero vector of G(λj,T) can also lie in ker(T−λiI)k for any k, which is precisely what membership in G(λi,T) would require.
What is the geometric meaning of dimG(λ,T) versus the geometric multiplicity dimker(T−λI)?
The algebraic multiplicity d=dimG(λ,T) counts how many times (z−λ) divides the characteristic polynomial, while the geometric multiplicity dimker(T−λI) counts actual linearly independent eigenvectors. Always dimker(T−λI)≤dimG(λ,T)=d, with equality exactly when T restricted to G(λ,T) has zero nilpotent part, i.e. when T is diagonalizable on that block. The gap between the two multiplicities measures precisely how far T is from being diagonalizable at λ.