Start with and keep applying the matrix with rows and , and the vectors , then , then appear. The starting vector and its first image are independent, the second image is already a combination of them, and from then on no new direction can ever appear. This lesson shows that this first dependence settles a lot at once, namely an invariant subspace with a natural basis, the matrix of the operator restricted to that subspace, and a recurrence that every later vector obeys. It also explains why some starting vectors reach the whole space and others do not. Throughout, is or , is a finite-dimensional vector space over , a square matrix stands for the operator , and a tuple such as stands for a column vector.
Key terms
Orbit of a vector
Definition Let be linear and let . The orbit of under is the sequence
with . Suppose . Any vectors of are dependent, so there is a smallest positive integer for which are linearly dependent, and . By the choice of the vectors are independent. In a nontrivial dependence among the coefficient of is not zero, since otherwise the earlier vectors would be dependent. Dividing by it gives the first dependence
and the scalars are unique because the earlier vectors are independent. Equivalently, for the monic polynomial
In plain words Apply to one vector again and again and keep the outputs in order. Each new output either lies outside the span of the earlier vectors or is a combination of them. The first time it is a combination, the coefficients of that combination define the polynomial .
Example Let
act on and take . The orbit starts , , , . The first two vectors are independent and , so and the first dependence is . Watch the signs here. The coefficients are and , because moving everything to one side gives , so .
Cyclic subspace
Definition Let be linear and . The cyclic subspace generated by is the span of its orbit,
For it is . For , let and come from the first dependence. Then the orbit basis is a basis of , so . The subspace is invariant under , and it is contained in every -invariant subspace that contains . The minimal polynomial of the restriction of to , written , is . A polynomial satisfies exactly when divides , so in particular divides the minimal polynomial .
For the proof, let be the span of the orbit basis. The operator sends to , which lies in for , and it sends to , which lies in by the first dependence. By linearity maps into , and induction on puts every in . Hence , and any invariant subspace containing contains every and therefore . Polynomials in commute, so for every , and vanishes on . A nonzero polynomial of degree less than that vanished on would vanish at , which would be a dependence among the independent vectors . So is the monic annihilating polynomial of least degree for . Finally, if , the same commuting argument shows that vanishes on , so divides . Conversely gives .
In plain words The cyclic subspace is everything you can build from by applying repeatedly and taking linear combinations. It is the smallest invariant subspace containing , and the vectors before the first dependence already span it, so drawing more orbit points never adds anything. The word cyclic refers to the repeated action, not to an orbit that closes up into a loop.
Example For the matrix above and the orbit basis is , so and on the whole plane. Indeed
and the entries of are , , and , all zero. A different seed can see less. The vector has , so is the line spanned by , with and . That polynomial divides , as it must.
Cyclic vector
Definition Let and let be linear. A vector is a cyclic vector for if , that is, if is a basis of . If has a cyclic vector with first dependence , then
Indeed, is the minimal polynomial of the restriction of to , which is itself, so has degree . By the Cayley-Hamilton theorem divides , which is also monic of degree , so the two are equal. Consequently an operator whose minimal polynomial has degree less than has no cyclic vector. The converse is also true over and , so has a cyclic vector exactly when , but that direction needs a longer argument and is not proved here.
In plain words A cyclic vector reaches the whole space by repeated action. When one exists, a single polynomial of degree records how acts, and the characteristic and minimal polynomials cannot differ.
Example For the matrix above, is a cyclic vector for on and is not. Now let act on . Its minimal polynomial has degree , so has no cyclic vector. The seed gives and , so it generates a plane, and the seed is an eigenvector for and generates only a line. A scalar matrix on a space of dimension at least is the extreme case, since every nonzero seed generates just its own line.
Companion matrix
Definition Let be a monic polynomial of degree with coefficients in . Its companion matrix is the matrix with ones directly below the diagonal, the negated coefficients in the last column, and zeros elsewhere,
If has first dependence , then the matrix of on in the ordered orbit basis is . Column holds the coordinates of the image of the th basis vector. The operator sends each orbit basis vector to the next one, which gives the ones below the diagonal, and it sends the last one to , whose coordinates come from the first dependence. Since for and is the last column, the vector is a cyclic vector for on with first dependence , so
Some books use the transpose, with the negated coefficients in the last row. It has the same characteristic polynomial.
In plain words In an orbit basis the operator just moves each basis vector to the next one, until the last, where the first dependence takes over. The last column stores the coefficients of with their signs reversed.
Example For we have and , so the last column is and is the matrix from the start. The same polynomial appears for and the seed , so the restriction of to the plane spanned by and also has matrix in the basis . The orbit matrix with columns and is and has no inverse, so the restricted matrix comes from coordinates in that basis instead. The image has coordinates , and the image has coordinates . For degree three take
whose companion matrix is
Linear recurrence
Definition Let be monic with coefficients in . A sequence of scalars, or of vectors in , satisfies the linear recurrence with polynomial if
for every . The orbit of a nonzero vector satisfies the recurrence of its first dependence , because applying to gives , which is the recurrence at step . For every linear functional , the scalar sequence satisfies the same recurrence, because is linear. The orbit also satisfies the recurrence of any monic polynomial with , such as or , and among these the recurrence of has the least order.
In plain words Once one dependence among consecutive images is known, every later image follows from the previous images with the same coefficients at every step. Any fixed linear measurement of the images inherits the same rule.
Example For and the recurrence is . From and it gives
which agrees with , and the next vector is . The second coordinates obey , and induction shows , because . The recurrence belongs to the seed. The orbit of satisfies the shorter rule .
Check yourself
- Let be the matrix with rows and . Find the cyclic subspaces generated by and by , and the matrix of in the orbit basis of .
Show the answer
The seed is an eigenvector, since . So , the cyclic subspace is the line spanned by , and . For the orbit starts , , . The first two are independent, and
so , the vector is cyclic with , and . With and the matrix in the orbit basis is
As a check, its trace and determinant match those of .
- Write the companion matrix of and check its trace and determinant against . What goes wrong if the signs in the last column are not reversed?
Show the answer
Here , and , so the last column is and
For a matrix the coefficient of in the characteristic polynomial is minus the trace, and the constant term is . The trace is and expanding along the first row gives , which match the coefficient and the constant term . Copying the coefficients without reversing their signs gives the last column , a matrix with trace whose characteristic polynomial is , a different polynomial.
- Does have a cyclic vector? Does ? For the first, find the recurrence satisfied by the orbit of .
Show the answer
For the orbit of starts , , . The matrix with these columns has determinant , so they form a basis and is a cyclic vector. Then , and the orbit satisfies
For instance . Not every seed works, though. The seed is an eigenvector and generates only a line. For the minimal polynomial is , of degree , so has no cyclic vector, even though its characteristic polynomial has degree .