The Jordan Canonical Form
Not every linear operator is diagonalizable. So a natural question arises. What is the simplest matrix form we can always achieve? This holds no matter how badly an operator fails to be diagonalizable. Consider an operator whose only eigenvalue is , but which is not a scalar multiple of the identity. Diagonalization is impossible here. Yet the operator still has a rigid, describable structure. This structure is built from chains of vectors linked by the equation . The Jordan Canonical Form answers the question completely. Over an algebraically closed field, every operator is similar to an essentially unique block-diagonal matrix. This matrix is built from elementary pieces called Jordan blocks. Each block has a single eigenvalue sitting on the diagonal. It also has a fringe of 's just above it. This matters because diagonalizability is the exception, not the rule. Yet almost every structural question about an operator reduces to understanding this canonical form. This includes computing , solving linear ODEs , understanding the structure of a nilpotent map, and classifying conjugacy classes of matrices. The Jordan form is the finest classification of similarity available for matrices over (or any algebraically closed field). Two matrices are similar if and only if they have the same Jordan form. This holds up to reordering of blocks. It converts an algebraic question (is similar to ?) into a combinatorial one. That question becomes: do and have the same multiset of Jordan blocks? The big picture starts with the Generalized Eigenspace Decomposition. It already splits any operator's domain into a direct sum of generalized eigenspaces, one for each eigenvalue. The operator restricted to each such piece is "eigenvalue times identity plus nilpotent." What remains is to understand nilpotent operators completely. It turns out every nilpotent operator on a finite-dimensional space decomposes into a direct sum of cyclic nilpotent pieces. These are chains of vectors annihilated in sequence by the operator. Reassembling these chains, shifted by , gives the Jordan blocks. This article builds the Jordan form from the generalized eigenspace decomposition. It states and proves the existence and uniqueness theorem in full. It works two nontrivial numerical examples. It also shows exactly which hypothesis (algebraic closure) is indispensable — and what happens when it fails. This foreshadows The Rational Canonical Form as the field-independent alternative.
- The Jordan Canonical Form expresses any operator on a finite-dimensional space over an algebraically closed field as a direct sum of Jordan blocks.
- A Jordan block is a upper triangular matrix with on the diagonal and 's on the superdiagonal.
- Existence follows from the Generalized Eigenspace Decomposition plus a structure theorem for nilpotent operators (cyclic decomposition).
- Uniqueness (up to reordering of blocks) follows from counting ranks of powers of , which are similarity invariants.
- The number and sizes of Jordan blocks for eigenvalue are determined by the sequence for
- Key idea: every operator over an algebraically closed field is, up to a change of basis, "diagonal plus a controlled nilpotent shift" made of chains of generalized eigenvectors.
Where this lives today
The Jordan Canonical Form underlies the theory of linear ordinary differential equations: solving reduces to computing , and has an explicit closed form involving times a polynomial in of degree up to , directly explaining resonance terms like that appear when eigenvalues are repeated. In control theory and signal processing, the Jordan form governs the long-term behavior of discrete linear systems , since grows like times a polynomial in , determining stability margins precisely when . In numerical analysis, the extreme sensitivity of the Jordan form to perturbation (a famous cautionary example) motivates why practical algorithms instead compute the more stable Schur form or singular value decomposition rather than attempting to compute Jordan blocks directly from floating-point data. In pure mathematics, the Jordan form is the model case for the general theory of modules over a principal ideal domain applied to -modules, connecting directly to The Rational Canonical Form and to the classification of finitely generated abelian groups via an entirely parallel structure theorem.
Ask the tutor: How do I explicitly construct a Jordan basis (the change-of-basis matrix ) once I know the block sizes, especially when an eigenvalue has more than one block of the same size?
Let be a scalar and an integer. The Jordan block is the matrix
that is, for all , for , and all other entries are . Let be a finite-dimensional vector space over a field , and let be a linear operator. We say is in Jordan Canonical Form with respect to a basis of if the matrix is block diagonal,
a direct sum of Jordan blocks (not necessarily with distinct , and the not necessarily distinct from each other). We say has a Jordan Canonical Form (or is Jordanizable) if such a basis exists. Equivalently, a square matrix over has a Jordan Canonical Form if is similar to a direct sum of Jordan blocks.
Intuition
Start with the easiest possible case beyond diagonal matrices: a single Jordan block with eigenvalue . This is the nilpotent shift operator on that sends the standard basis vector (and ). Picture a chain of beads threaded on a string: applying the operator slides every bead down one position, and the bottom bead falls off (maps to ). This chain structure, , is the entire content of a Jordan block with ; adding back just tilts every bead's "resting value" from to , since still behaves like the shift. The full Jordan Canonical Form theorem says that every operator's action can be understood, basis vector by basis vector, as a union of such chains — one chain of some length for each eigenvalue, possibly several chains per eigenvalue of possibly different lengths. Diagonalizable operators are the special case where every chain has length (no shifting at all, just scaling). The generalized eigenspace decomposition already isolates each eigenvalue's contribution; what remains, and what the Jordan theorem supplies, is the precise chain-length data (a partition of the dimension of each generalized eigenspace) that pins down the operator up to similarity.
Let be a finite-dimensional vector space over an algebraically closed field , with , and let be a linear operator.
(Existence.) There is a basis of such that is a direct sum of Jordan blocks.
(Uniqueness.) This decomposition is unique up to the order of the blocks: if and are two bases such that and are each direct sums of Jordan blocks, then the multiset of Jordan blocks (each block specified by its eigenvalue and size) occurring in equals the multiset occurring in . Consequently, two matrices are similar if and only if they have the same Jordan Canonical Form up to reordering of blocks.
Let be a finite-dimensional vector space over any field , and let be nilpotent, i.e. for some . Then there exist vectors and positive integers such that
each summand is -invariant, , and . Moreover the multiset is uniquely determined by : it equals the multiset of positive part-sizes of the partition of dual (via conjugate partitions) to the sequence , ; equivalently the number of equal to a given value is
Existence, by strong induction on . If the statement holds vacuously. Suppose and the result holds for all nilpotent operators on spaces of dimension less than . Let be minimal with (if , take and every basis vector generates its own length- chain, done). Consider . Since is not invertible (it is nilpotent on a nonzero space, as forces ), , so . Also , so is a well-defined nilpotent operator on (nilpotent because ). By the inductive hypothesis applied to , there exist and lengths with
Since each , choose with . Then and , so generates a cyclic chain of length :
Claim 1: The vectors are linearly independent, and has dimension . Indeed, note span the same set as , which together over all are linearly independent because they form (a subfamily equal to) the basis of produced by the inductive hypothesis. Adding the vectors (for ) preserves independence: any linear dependence among , applying , gives a dependence among the () alone (since eliminates the terms only if their coefficients vanish after applying — more precisely, suppose . Applying : in , forcing (by independence of the basis of ) all and all for with nonzero terms — since the basis of is exactly , this forces every coefficient in that relation to be , in particular for all ; then the original relation reduces to , i.e. , forcing too). Hence Claim 1 holds. Claim 2: , and we may extend a basis to reach all of . Let (direct because the spanning sets found in Claim 1 are independent). We have . Now pick any complement: extend the linearly independent set (a basis for ... — more directly) as follows. Since has dimension (it is spanned by the "top" vectors of each chain in , by the rank–nullity structure of the cyclic decomposition of ), and has dimension (rank–nullity for , since is surjective onto by definition of ), we may choose a complement of inside , with
Choose a basis of (so ), each satisfying , i.e. each generates a chain of length : . Now verify : dimensions add correctly,
so it suffices to show and is direct with the rest — but we show directly by showing the total spanning set is independent and has size . Independence of : any dependence, after applying , kills all the () terms (since for ) and all terms shift down by one power; arguing as in Claim 1 this forces the coefficients of for to vanish, and separately the coefficients of () to vanish by Claim 1's argument, leaving a dependence purely among , which are independent by construction (basis of ) and independent from because was chosen as a complement to inside , hence and in particular (as ... since each generator , , was chosen with , so , and is disjoint from that span by the complement construction). Counting total dimension: . A linearly independent spanning-sized set is a basis, so , completing the inductive step with chain lengths for and for .
Uniqueness of the multiset . For , restricted to a single chain summand (with ) contributes exactly to the rank of on (since maps the basis of to , of which are nonzero and, being a sub-list of an independent chain basis mapped into disjoint summands, contribute independently to the rank of on the whole space — direct sums of -invariant subspaces make block diagonal, and rank adds over blocks). Hence
The right side is exactly the "partition rank function" of the partition of ; a classical identity for partitions gives
which depends only on (not on any choice of chains), proving the multiset is an invariant of alone.
∎Proof of the main theorem, using the Lemma. Existence. By the Generalized Eigenspace Decomposition, since is algebraically closed, the characteristic polynomial of splits into linear factors, and decomposes as a direct sum of generalized eigenspaces
where are the distinct eigenvalues of and . Each is -invariant, and on the operator is nilpotent by definition of generalized eigenspace (every vector is annihilated by some power of , and finite-dimensionality gives a uniform bound, namely ). Apply the Lemma to on : we obtain a basis of consisting of chains with . On such a chain, writing for (so is the "bottom," annihilated by ), we get
i.e. and , so and . In the ordered basis of this chain, the matrix of restricted to is exactly : column records , giving on the diagonal and a single in row , i.e. the superdiagonal. Concatenating the chain-bases over all chains within , and then over all , produces a basis of (a basis of the whole space, since the direct-sum to and each chain-basis is a basis of its summand) in which is block diagonal with each block a Jordan block . This proves existence. Uniqueness. Suppose is any basis with a direct sum of Jordan blocks . First, the set of distinct 's occurring must be exactly the set of eigenvalues of : a block-diagonal matrix has characteristic polynomial equal to the product of the characteristic polynomials of its blocks, and since is upper triangular with repeated on the diagonal; hence the multiset of all (with multiplicity each) is exactly the multiset of roots of the characteristic polynomial of , which is similarity-invariant. Fix an eigenvalue of . Let be the union of the basis vectors belonging to the Jordan blocks (those blocks among with eigenvalue exactly ), and let . Direct computation on a single block shows acts on the span of 's basis exactly as the nilpotent shift, hence is nilpotent there, and for , has which is invertible (upper triangular with nonzero diagonal entries ). Since is block diagonal, (the sum of blocks with that exact eigenvalue) is precisely
because on , is nilpotent (kernel eventually all of under a high enough power), while on the complementary sum of other blocks, is invertible (each block invertible for , so the whole block-diagonal restriction is invertible, being a direct sum of invertible maps), hence contributes nothing to the kernel of any power. But is exactly the generalized eigenspace, which is intrinsically defined from alone (no basis choice). Hence is forced, independent of which valid Jordan basis we started with. Within , the restriction is a fixed nilpotent operator (determined by and alone), and the basis exhibits as a direct sum of shift-chains of lengths (the sizes of the blocks with eigenvalue ) — precisely the cyclic decomposition data of from the Lemma. By the uniqueness clause of the Lemma, the multiset of chain lengths is determined by alone via
which does not depend on at all — it is a formula purely in terms of and . Therefore the number of Jordan blocks of size with eigenvalue is the same for any valid Jordan basis . Since and were arbitrary, the entire multiset of Jordan blocks (eigenvalue and size for each) is an invariant of , proving uniqueness up to reordering. Similarity criterion. If are similar, say , then a Jordan basis for pushed forward by is a Jordan basis for giving the identical block multiset, since similar operators represent the same abstract linear map on isomorphic spaces up to change of basis, so their Jordan forms (existing by the theorem) coincide up to block order. Conversely, if and have the same Jordan block multiset, then each is similar to the same block-diagonal matrix (via their respective Jordan bases), hence and for invertible , giving , so and are similar.
∎Worked examples
Example 1 (a matrix, single eigenvalue, nontrivial block pattern). Let and let act on so that satisfies, with respect to the standard basis ,
So has three chains: (length ), (length ), and (length ). We verify this via ranks, to illustrate the uniqueness formula rather than just reading off the chains. Matrix of (columns are images of ):
Compute ranks directly. has two nonzero rows worth of pivots: columns contribute rank from the first chain, column from the second; so (images are hit, all independent). : apply twice. , , all others . So . (longest chain has length ), so , and for . Also . Now apply the counting formula :
- : .
- : .
- : .
- : all ranks involved are , giving .
So there is exactly one block of size , one of size , one of size — matching our construction exactly, and confirming . The Jordan Canonical Form of is
Example 2 (two eigenvalues, mixed block sizes, computed from a concrete matrix). Let
— but suppose we are only handed the characteristic and minimal polynomial data and rank computations, as would happen from a genuinely non-block-diagonal input; we determine the Jordan form purely from invariants to demonstrate the method. Say we are told:
- Characteristic polynomial: .
- (computed by row reduction), so .
- , so ; since the generalized eigenspace for has dimension (algebraic multiplicity) we also know (it stabilizes once the kernel reaches dimension , i.e. stabilizes at ).
- , so ; generalized eigenspace for has dimension so (stabilizes at ).
For : apply the formula with restricted to (dimension ), using the global ranks intersected appropriately — more directly, since has dimension and the restricted operator's rank sequence (restricted to ) is . We use that overall (and all of this kernel lies in , a general fact about generalized eigenspaces), so restricted to : , . Since and this kernel is entirely inside too, restricted rank . Since has dimension , there, so . Counting: : . : . : . So there is exactly one Jordan block of size for : it is , consuming all dimensions of — consistent since (a single block always contributes exactly to the eigenspace dimension). For : has dimension ; restricted ranks: , and since (restricted to , all -dimensional kernel lies inside), ; since dimension is , there so . Counting: : . : . So one block of size : . Total Jordan form: , matching exactly the block-diagonal matrix we started with — confirming the invariant-based method recovers the true answer even without directly reading off chains from an already-block-diagonal matrix.
The theorem requires to be algebraically closed (equivalently: the characteristic polynomial of splits into linear factors over ). Drop this hypothesis and existence of a Jordan Canonical Form can fail entirely. Let and let
the rotation matrix, viewed as an operator on . Its characteristic polynomial is , which is irreducible over (its roots are not real). Suppose for contradiction that were similar over to a direct sum of Jordan blocks with each . Since similar matrices have identical characteristic polynomials, and the characteristic polynomial of any direct sum of Jordan blocks with real eigenvalues is — a product of real linear factors — this would force to factor into real linear factors, contradicting irreducibility. Thus no basis of puts into Jordan Canonical Form: has no real eigenvalues at all, so in particular no eigenvector, and Jordan blocks always require at least one eigenvector per block (the "bottom" of each chain). The obstruction is purely the failure of algebraic closure: viewed instead as an operator on (i.e., after extending scalars), does have eigenvalues and is even diagonalizable there, similar to . This is exactly the gap that The Rational Canonical Form is designed to fill: it produces a canonical similarity-invariant matrix form (built from companion matrices of invariant factors) that exists over any field, at the cost of using slightly larger, less transparent blocks than single Jordan blocks when the characteristic polynomial does not split.
Scope, edge cases & common traps
| Situation | What happens |
|---|---|
| algebraically closed (e.g. ) | Jordan form always exists and is unique up to block order. |
| , characteristic polynomial splits over | Jordan form still exists (only linear factors were used in the proof). |
| , characteristic polynomial has non-real roots | No Jordan form over ; need or The Rational Canonical Form. |
| diagonalizable | Jordan form has all blocks of size ; recovers ordinary diagonalization. |
| Repeated eigenvalue, multiple blocks of same size | Uniqueness is only up to order; e.g. has an ambiguous choice of which basis vectors form which chain, but the block multiset is forced. |
| Confusing algebraic and geometric multiplicity | Algebraic multiplicity of = sum of sizes of all -blocks; geometric multiplicity = number of -blocks (dimension of the ordinary eigenspace), not their sizes. |
The mathematician behind it
Origins · 1 minMarie Ennemond Camille Jordan and how this was discovered
Camille Jordan was a French mathematician and engineer, professor at the École Polytechnique and the Collège de France, whose Traité des substitutions et des équations algébriques (1870) laid foundational work in group theory and linear algebra. He introduced the canonical block-diagonal decomposition for matrices (over what we would now call an algebraically closed field, in the context of substitution groups) in the course of his systematic study of the structure of linear substitutions, motivated by Galois theory and the classification of finite groups of linear transformations. Jordan is also known for the Jordan–Hölder theorem in group theory and the Jordan curve theorem in topology, but the canonical form bearing his name arose specifically from the linear-algebraic and group-theoretic study of matrix similarity classes.
Why must every Jordan block have exactly one eigenvector, not more?
A Jordan block satisfies equal to the nilpotent shift matrix with 's on the superdiagonal and elsewhere. Its kernel (the eigenspace for within that block) is spanned by exactly the first standard basis vector, a one-dimensional space, regardless of . So each Jordan block, no matter how large, contributes only one dimension to the eigenspace — hence exactly one linearly independent eigenvector. This is why geometric multiplicity (dimension of the ordinary eigenspace) counts the number of Jordan blocks for that eigenvalue, while algebraic multiplicity counts the total size (sum of block sizes) — two different invariants that coincide only when every block has size , i.e. when is diagonalizable.
How is the Jordan Canonical Form related to diagonalization?
Diagonalization is the special case of the Jordan Canonical Form in which every Jordan block has size . A single Jordan block is just the matrix , so a direct sum of size- blocks is literally a diagonal matrix. The Minimal Polynomial and Diagonalizability criterion says is diagonalizable exactly when its minimal polynomial has no repeated roots; in Jordan-form language, this happens exactly when every Jordan block for every eigenvalue has size , since the minimal polynomial of a direct sum of Jordan blocks is where is the largest block size for eigenvalue — repeated roots in the minimal polynomial correspond precisely to blocks of size .
Do I need to actually find the chain vectors to determine the Jordan form, or can I just use ranks?
You can determine the block sizes and multiplicities — the full similarity class — using only the rank formula applied to for each eigenvalue , without ever constructing an explicit Jordan basis. This is enough to answer "what is the Jordan form of this matrix" and to test similarity between two matrices. However, if you need an explicit change-of-basis matrix with in Jordan form (for instance, to solve a linear ODE explicitly), you do need to construct the actual generalized eigenvectors and chains, which requires solving nested systems starting from vectors in .
Why doesn't the Jordan Canonical Form exist over every field, unlike, say, row echelon form?
Row echelon form is achieved purely by elementary row operations, which are always available over any field, and it doesn't attempt to encode eigenvalue information — it's not a similarity invariant, only an equivalence invariant (allowing independent row and column operations). The Jordan form, by contrast, is a similarity invariant: it requires expressing as diagonal-plus-nilpotent pieces glued exactly at the eigenvalues, and this construction leans on the Generalized Eigenspace Decomposition, which itself requires the characteristic polynomial to split into linear factors. Over a non-algebraically-closed field like or , an irreducible quadratic or higher-degree factor in the characteristic polynomial signals an eigenvalue that simply does not exist in that field, so no basis over that field can diagonalize-plus-nilpotent-decompose the operator around it.
What is the relationship between the Jordan form and the minimal and characteristic polynomials?
The characteristic polynomial of equals where is the algebraic multiplicity, which in Jordan-form terms is the sum of the sizes of all Jordan blocks for eigenvalue — this recovers exactly the total dimension of . The minimal polynomial equals where is the size of the largest Jordan block for , because is needed to annihilate the longest chain, and no smaller power suffices, while for contributes independently on other generalized eigenspaces. Both polynomials are recoverable from the Jordan form, but neither one alone recovers the full Jordan form when there is more than one block per eigenvalue.
Can the Jordan Canonical Form of a real matrix with all real eigenvalues be computed while staying entirely within real arithmetic?
Yes. The existence proof only uses the fact that the characteristic polynomial splits into linear factors over the field in question — it never uses complex conjugation or any property special to beyond algebraic closure. If a real matrix has characteristic polynomial factoring completely into real linear factors (equivalently, all eigenvalues are real, counted with multiplicity), then the generalized eigenspace decomposition and the cyclic decomposition of each nilpotent piece can be carried out entirely with real vectors and real arithmetic, producing a genuine real Jordan basis. Only when some eigenvalues are non-real does one need to either pass to or use the real Jordan form with real rotation-scaling blocks instead of complex Jordan blocks.