Advanced linear algebra turns linear maps into structural invariants. Bases and matrices are coordinates; the underlying map or module is the object. Canonical forms are useful because they expose invariants that do not depend on a particular basis. This page consolidates the relevant material from the supplied algebra source into a stand-alone handbook chapter.
Learning pathAdvanced Linear Algebra
LevelAdvanced
FormatHandbook guide
Read time15 min
Executive summary
This chapter develops bilinear forms and orthogonality as part of a connected advanced-algebra learning sequence. The emphasis is on definitions, hypotheses, structural results and repeatable methods rather than historical narrative.
The source material is theorem-rich. Accordingly, the handbook presentation separates vocabulary from results and then adds a verification workflow so that each statement can be applied safely. Mathematical examples in the source are treated as examples, not as universal rules.
Problem-solving workflow
Identify the field or coefficient ring and the vector space or module.
Choose bases only after deciding what structure should be preserved.
Represent the map by a matrix and track how the matrix changes under a basis change.
Use invariant subspaces, cyclic decomposition or elementary divisors to reduce the problem.
Read structural information from the resulting normal or canonical form.
Translate the matrix conclusion back into a basis-independent statement.
Core definitions
Definition
A bilinear form (or inner product) on V is a bilinear function f : V × V →k. The ordered pair (V, f ) is called an inner product space. Bilinear Forms Of course, (kn, f ) is an inner product space if f is the familiar dot product f (u, v) = i uivi, where u = (u1, . . . , un)t and v = (v1, · · · , vn)t (the superscript t denotes transpose; remember that the elements of kn are n×1 column vectors). In terms of matrix multiplication, we have f (u, v) = utv. There are two types of bilinear forms of special interest.
Definition
Let f be a bilinear form on a vector space V over a field k, and let E = e1, . . . , en be a basis of V . Then an inner product matrix of f relative to E is A = [ f (ei, e j)]. Suppose that (V, f ) is an inner product space, e1, . . . , en is a basis of V , and A = [ f (ei, e j)] is the inner product matrix of f relative to E. If b = biei and c = ciei are vectors in V , then f (b, c) = f ( biei, ciei ) = i, j bi f (ei, e j)c j. If B and C denote the column vectors (b1, . . . , bn)t and (c1, . . . , cn)t, respectively, then this last equation can be written in matrix form: f (b, c) = Bt AC. Thus, an inner product matrix determines f completely. Bilinear Forms
Definition
If (V, f ) is an inner product space and W ⊆V is a subspace of V , then the left orthogonal complement of W is W ⊥L = {b ∈V : f (b, w) = 0 for all w ∈W}; the right orthogonal complement of W is W ⊥R = {c ∈V : f (w, c) = 0 for all w ∈W}. Moreover, W ⊥L = W ⊥R if f is either symmetric or alternating, in which case we write W ⊥. Let (V, f ) be an inner product space, and let A be the inner product matrix of f relative to a basis e1, . . . , en of V . We claim that b ∈V ⊥L if and only if b is a solution of the homogeneous system Atx = 0. If b ∈V ⊥L. then f (b, e j) = 0 for all j. In matrix terms, b = (b1, . . . , bn)t and Bt A = 0; transposing, b is a solution of the homogeneous system Atx = 0. The proof of the converse is left to the reader. A similar argument shows that c ∈V ⊥R if and only if c is a solution of the homogeneous system Ax = 0. Bilinear Forms
Definition
A hyperbolic plane over a field k is a two-dimensional vector space over k equipped with a nonzero alternating bilinear form. We have just seen that every two-dimensional alternating space (V, f ) in which f is not identically zero has an inner product matrix A = 0 1 −1 0 .
Definition
Let (V, f ) be a symmetric space, and let E = e1, . . . , en be a basis of V . If e1, . . . , en is an orthogonal basis of a symmetric space (V, f ), then V = ⟨e1⟩⊕· · ·⊕ ⟨en⟩is an orthogonal direct sum. In Corollary 9.76, we saw that if (V, f ) is a symmetric space with f nondegenerate, and if e1, . . . , en is a basis of V , then there exists a basis b1, . . . , bn of V with f (ei, b j) = δi j. If E is an orthonormal basis, then we can set bi = ei for all i.
Definition
Let V be an a vector space over a field k. A quadratic form is a function Q : V →k such that (i) Q(cv) = c2Q(v) for all v ∈V and c ∈k; (ii) the function g : V × V →k, defined by g(u, v) = Q(u + v) −Q(u) −Q(v), is a bilinear form.
Definition
Let (V, f ) be an inner product space, where V is a finite-dimensional vector space over a field k and f is a nondegenerate bilinear form. (i) If f is alternating, then Isom(V, f ) is called the symplectic group, and it is denoted by Sp(V, f ). (ii) If f is symmetric, then Isom(V, f ) is called the orthogonal group, and it is denoted by O(V, f ). Bilinear Forms As always, a choice of basis E of an n-dimensional vector space V over a field k gives an isomorphism µ: GL(V ) →GL(n, k), the group of all nonsingular n × n matrices over k. In particular, let (V, f ) be an alternating space with f nondegenerate, and let E = x1, y1, . . . , xm, ym be a symplectic basis of V (which exists, by Corollary 9.80); recall that n = dim(V ) is even; say, n = 2m. Denote the image of Sp(V, f ) by Sp(2m, k). Similarly, if (V, f ) is a symmetric space with f nondegenerate, and E is an orthogonal basis (which exists when k does not have characteristic 2, by Theorem 9.81), denote the image of O(V, f ) by O(n, f ). Let us find adjoints when the bilinear form is symmetric or alternating.
Definition
An R-algebra A is a graded R-algebra if there are R-submodules Ap, for p ≥0, such that (i) A = p≥0 Ap; (ii) For all p, q ≥0, if x ∈Ap and y ∈Aq, then xy ∈Ap+q; that is, Ap Aq ⊆Ap+q. An element x ∈Ap is called homogeneous of degree p. Notice that 0 is homogeneous of any degree, but that most elements in a graded ring are not homogeneous and, hence, have no degree. Note also that any product of homogeneous elements is itself homogeneous.
Principal results and structural facts
Key result
Let k be a field of characteristic ̸= 2, and let f be a bilinear form defined on a vector space V over k. Then there are unique bilinear forms fs and fa, where fs is symmetric and fa is alternating, such that f = fs + fa.
Key result
If (V, f ) is an inner product space and if A and A′ are inner product matrices of f relative to different bases of V , then there exists a nonzero a ∈k with det(A′) = a2 det(A). Consequently, A′ is nonsingular if and only if A is nonsingular.
Key result
Let (V, f ) be an inner product space, and let e1, . . . , en be a basis of V . Then f is nondegenerate if and only if f ( , e1), . . . , f ( , en) is a basis of the dual space V ∗(we call the latter the dual basis).
Key result
Let (V, f ) be an inner product space with f nondegenerate. If e1, . . . , en is a basis of V , then there exists a basis b1, . . . , bn of V with f (ei, b j) = δi j.
Key result
, there are b1, . . . , bn ∈V with gi = f ( , bi) for all i. Commutativity of the diagram gives f (ei, b j) = ev(ei, g j) = δi j. •
Key result
Let (V, f ) be an alternating space, where V is a vector space over a field k. If f is nondegenerate, then there is an orthogonal direct sum V = H1 ⊕· · · ⊕Hm, where each Hi is a hyperbolic plane.
Key result
Let (V, f ) be an alternating space in which f is nondegenerate,14 and let A be an inner product matrix for f (relative to some basis of V ). (i) There exists a symplectic basis x1, y1, . . . , xm, ym for V , and A is a 2m ×2m matrix for some m ≥1. (ii) A is congruent to a matrix direct sum of blocks of the form −1 , and the latter is congruent to I −I , where I is the m × m identity matrix. (iii) Every nonsingular skew-symmetric matrix A over a field k is congruent to a direct sum of 2 × 2 blocks −1 .
Key result
If f is a symmetric bilinear form on a vector space V over R of dimension m, then there is an orthogonal direct sum V = W+ ⊕W−⊕W0, where f |W+ is positive definite, f |W−is negative definite, and f |W0 is identically 0. Moreover, the dimensions of these three subspaces are uniquely determined by f .
Key result
Let (V, f ) be an inner product space, where f is a nondegenerate bilinear form, let E = e1, . . . , en be a basis of V , and let A be the inner product matrix relative to E. Then ϕ ∈GL(V ) is an isometry if and only if its matrix M = E[ϕ]E satisfies the equation Mt AM = A.
Key result
If (V, f ) is an inner product space whose bilinear form f is nondegenerate, then every linear transformation T : V →V has an adjoint.
Key result
Let (V, f ) be an inner product space whose bilinear form f is nondegenerate. If T : V →V is a linear transformation with adjoint T ∗, then T is an isometry if and only if T ∗T = 1V , in which case T ∗= T −1.
Key result
Let (V, f ) be an alternating space, where V is a 2m-dimensional vector space over a field k and f is nondegenerate, and let E be a symplectic basis ordered as x1, . . . , xm, y1, . . . , ym. The adjoint of a matrix B = P Q S T relative to E, partitioned into m × m blocks, is B∗= T t −Qt −St Pt . Remark. It follows that B ∈Sp(2m, k) if and only if B∗B = I. ◀
Key result
(i) Let (V, f ) be an inner product space, where V is a finite-dimensional vector space over a field k and f is a nondegenerate bilinear form. The stabilizer GL(V ) f of f under the action on kV ×V is Isom(V, f ). (ii) If g : V × V →k lies in the same orbit as f , then Isom(V, f ) and Isom(V, g) are isomorphic; in fact, they are conjugate subgroups of GL(V ).
Key result
shows that det(P) = 1 for P ∈Sp(2, k) [it is true, for all m ≥1, that Sp(2m, k) ≤SL(2m, k)]. 9.60 If A is an m × m matrix with At A = I, prove that A A is a symplectic matrix. Conclude, if k is a finite field of odd characteristic, that O(m, k) ≤Sp(2m, k). 9.61 Let (V, f ) be an alternating space with f nondegenerate. Prove that T ∈GL(V ) is an isometry [i.e., T ∈Sp(V, f )] if and only if, whenever E = x1, y1, . . . , xm, ym is a symplectic basis of V , then T (E) = T x1, T y1, . . . , T xm, T ym is also a symplectic basis of V . 9.6 GRADED ALGEBRAS We are now going to use tensor products of many modules in order to construct some useful rings. This topic is often called multilinear algebra. Throughout this section, R will denote a commutative ring.
Source-grounded examples
Worked source example
(i) If V = k2 and its elements are viewed as column vectors, then det: V × V →k, given by a b , c d ↦det a c b d = ad −bc, is an example of an alternating bilinear form. More generally, define a function f : Cn × Cn →C by f (u, v) = j u jv j, where u = (u1, . . . , un)t, v = (v1, . . . , vn)t, and c denotes the complex conjugate of a complex number c. Such a function is not C-bilinear because f (u, cv) = c f (u, v) instead of cf (u, v). conjugate-symmetric forms are examples of sesquilinear forms; such forms can be constructed over any field k equipped with an automorphism of order 2 (to play the role of complex conjugation). ◀ Every bilinear form can be expressed in terms of symmetric and alternating bilinear forms.
Worked source example
(i) The polynomial ring A = R[x] is a graded R-algebra if we define Ap = {rx p : r ∈R}. The homogeneous elements are the monomials and, in contrast to ordinary usage, only monomials (including 0) have degrees. On the other hand, x p has degree p in both usages of the term degree. (ii) The polynomial ring A = R[x1, x2, . . . , xn] is a graded R-algebra if we define Ap = { rxe1 1 xe2 2 · · · xen n : r ∈R and ei = p } ; that is, Ap consists of all monomials of total degree p.
How to reason with these results
Most advanced-algebra problems become manageable when the representation is separated from the invariant structure. Begin with the definition, then decide whether the problem is asking for an elementwise calculation, a statement about a morphism, or a classification up to isomorphism. That choice determines the correct proof language.
When a theorem gives a structural conclusion, do not jump directly to the conclusion. Write the hypotheses next to the object you are studying and check them one by one. If a hypothesis fails, either strengthen the object, pass to a quotient or localisation where the theorem applies, or use a more elementary argument.
For computational work, record each transformation together with the equivalence relation it preserves. In algebra, row operations, similarity, quotienting, localisation and isomorphism preserve different kinds of information. A calculation is useful only when the preserved structure matches the question.
Common failure modes
Failure mode
Control
Confusing a linear map with one particular matrix representing it.
Return to the definition or theorem hypotheses and verify the missing condition before continuing.
Changing basis on only the domain or codomain when similarity requires a coordinated change.
Return to the definition or theorem hypotheses and verify the missing condition before continuing.
Assuming diagonalisation when the polynomial or field conditions do not permit it.
Return to the definition or theorem hypotheses and verify the missing condition before continuing.
Ignoring characteristic-dependent behaviour.
Return to the definition or theorem hypotheses and verify the missing condition before continuing.
Treating canonical-form calculations as mere row reduction without tracking the allowed equivalence relation.
Return to the definition or theorem hypotheses and verify the missing condition before continuing.
Verification checklist
The ambient set, ring, field, group, module or category has been stated.
Every operation and map used is well-defined in that setting.
The hypotheses of each structural result have been checked before use.
Representatives, coordinates or generators have not been confused with the underlying object.
Existence and uniqueness have been separated where both matter.
The final result has been checked against the original defining relation or universal property.
Quick questions
What should I identify first in a problem about bilinear forms and orthogonality?
Start with the ambient algebraic structure, its operation or maps, and the exact hypotheses. Most incorrect solutions begin by using a familiar rule that is not valid in the stated structure.
How should definitions be used in proofs?
Expand the definition at the point where it becomes useful. Definitions are not background prose; they are the conditions that determine what must be proved and which implications are available.
When is a structural theorem safer than direct calculation?
Use a structural theorem when its hypotheses are satisfied and the calculation would otherwise depend on arbitrary coordinates, representatives or generators. The theorem usually identifies an invariant that survives those choices.
How can a final answer be checked?
Substitute the result back into the defining relation, verify any required closure or map property, and check edge cases such as zero, the identity, the empty object or degenerate quotients where relevant.
Connections within the handbook
Source basis: supplied advanced algebra reference. Source-identifying authorship, publisher information, acknowledgements and biographical material are intentionally omitted. Mathematical terminology and results are retained in handbook form.